Early in the book, Nosta writes, “The solved can never touch the whole.”  That line lingers. It captures the essence of his argument. AI can solve, generate, synthesize, and accelerate. Yet something about the human experience of thinking, the struggle, the friction, the meaning-making, exists beyond resolution.

This tension defines the book. It is not anti-technology. Nosta is deeply engaged with AI and candid about its value. He describes large language models as tools that “move faster and connect more disparate concepts than our minds could ever manage on their own.”  He is equally clear that this capability introduces a subtle risk. We may begin to outsource not just tasks, but thought itself.

That distinction matters more than many may be willing to admit.

From Tools to Thought

One of the most compelling contributions of The Borrowed Mind is its framing of AI not as the next step in computing, but as a turning point in cognition. Nosta traces a clear arc. Gutenberg unlocked words. Google unlocked facts. AI, he argues, is unlocking thought. 

That progression is elegant, yet also unsettling. Words and facts could be externalized without fundamentally altering the structure of human reasoning. Thought is different. It is intimate. It is identity. It is how we become.

Nosta reminds us that thinking once required effort, a type of natural friction that created sparks of innovation. “The distance between question and answer created space for our discernment.”  Within that space, judgment formed, curiosity deepened, and understanding took root.

AI compresses that distance. It removes friction. It delivers coherence with remarkable speed.  One of the book’s most important insights emerges here. Coherence is not the same as understanding.

Nosta introduces the concept of “anti-intelligence,” describing it as “fluency without understanding. Coherence without experience.”  AI does not think. It mirrors the structure of thinking. It produces language that resembles reasoning without sharing its origin.

In health, where evidence, interpretation, and judgment must coexist, this distinction is not academic. It is operational. It shapes how clinicians trust tools, how leaders deploy them, and how patients ultimately experience care.

The Seduction of the Socratic Mirror

One of the most original sections of the book is Nosta’s description of the “Socratic Mirror.” He draws a parallel between classical dialogue and modern AI interaction. Socrates asked questions to surface the truth. AI, in a different way, reflects our thinking back to us, reframed, extended and sometimes clarified.

Nosta writes that the model “…does not tell me what to think but creates the conditions under which my own thinking could deepen.” This is where the book moves beyond critique and into possibility.

Used well, AI becomes a cognitive partner. It expands perspective, accelerates exploration, and invites iteration. In clinical research, patient engagement, and system design, this capacity holds enormous promise.

Nosta does not romanticize the relationship. He recognizes its asymmetry. The model has no interior life. It does not ponder. It does not carry consequence. It does not bear responsibility. That responsibility remains human.

Rethinking the Fear of Displacement

A persistent anxiety runs beneath every conversation about AI. Many fear it will become a job slayer, a force that displaces rather than elevates human contribution. That concern is understandable, yet not new.

Every meaningful advance in technology has reshaped how people work. The wheel did not eliminate labor. It redefined movement. The stethoscope did not replace physicians. It extended their ability to listen and interpret. The tollbooth transponder did not end transportation roles. It changed the flow and focus of human involvement. Each innovation shifted roles, demanded new skills, and expanded what people could do.  AI belongs in that lineage.

What distinguishes this moment is not the elimination of work, but the redistribution of cognitive effort. The real risk is not that machines will think for us, but that people may become less inclined to think for themselves. Nosta’s warning is subtle yet profound. Surrendering curiosity, judgment, and reflection to systems that generate answers with ease risks dulling the very faculties that define human intelligence.

This is why The Borrowed Mind is such an important read at this moment. It does not dismiss concerns around job displacement. It reframes it. The central challenge is not protecting roles as they exist today, but strengthening the uniquely human capacities no system can replicate. Creativity, discernment, ethical reasoning, and the ability to navigate ambiguity are not diminished by AI. They become more essential.

The book offers reassurance without complacency. The future of work will favor those who sharpen their thinking, engage deeply with ideas, and remain active participants in their own intellectual development. The machine is not the adversary. Neglecting the development of one’s own mind is a danger.

Composite Intelligence and the Limits of the Machine

Nosta introduces “composite intelligence” to describe the interaction between human and machine cognition. Composite does not mean blended into sameness. It means distinct contributions working in concert. The model brings speed and breadth. The human brings depth.

This triad becomes one of the most useful frameworks in this book. AI excels in velocity and scale. Depth, the slow transformation of understanding, remains human. As Nosta writes, “Models do not ponder.” 

In health, this distinction is profound. Data can inform. Algorithms can suggest. The act of deciding, especially in moments of uncertainty, requires something more. It requires what Nosta elevates as the defining human contribution. Virtue.

Drawing on Aristotle’s concept of practical wisdom, Nosta reminds us that judgment is forged through experience, consequence, and accountability. A model can generate options. It cannot live with outcomes.

This is where the book resonates most deeply for those working in health. Intelligence is becoming abundant. Discernment is becoming scarce and, therefore, more valuable.

The Risk of the Borrowed Mind

The book’s title is not metaphorical. It is a warning. Nosta argues that as engagement with AI deepens, internal dialogue begins to change. The model becomes a cognitive tuning fork, subtly shaping how questions are framed, how ideas are explored, and how answers are anticipated. This dynamic is not inherently negative. It can elevate thinking, accelerate learning, and make complex domains more accessible. Dependency remains the concern.

Reliance on generated thought risks weakening the muscle of original thinking. Access can be mistaken for understanding. Individuals may become, in Nosta’s words, “cognitive clones.” 

This concern is particularly relevant in health ecosystems already strained by time, complexity, and administrative burden. The temptation to offload cognitive work will be strong. The discipline to remain intellectually engaged will be essential.

A Book About AI That Is Not About AI

What makes The Borrowed Mind stand apart is that it is not ultimately about technology. It is about humanity. Nosta writes, “This book is not really about technology. It is about you.”  That idea anchors this work.

Readers are challenged to consider what it means to remain “the authors of our own minds.”  Not passive recipients of generated insight, but active participants in meaning-making.

This question sits at the center of the health ecosystem’s future. As AI becomes embedded in clinical workflows, research, and patient engagement, the issue is not whether it will improve efficiency. It will.

The deeper question is whether it will deepen humanity or dilute it. Will it create space for clinicians to think more deeply, connect more meaningfully, and act more wisely? Or will it create a system that values speed over reflection, output over understanding, and coherence over truth?

Nosta offers no simple answers. He offers a framework for asking better questions.

The post “The Borrowed Mind” – Reclaiming Thought in an Age That Wants to Do It For Us appeared first on Medika Life.

This story also ran on NBC News. See below.

West Virginia’s Public Employees Insurance Agency enrolls nearly 215,000 people — state workers, as well as their spouses and dependents. The new law, which will take effect June 10, will allow plan members who have been approved for a course of treatment to pursue an alternative, medically appropriate treatment of equal or lesser value without the need for another approval from the state-based health plan.

“This legislation is rooted in a simple principle: if a treatment has already been approved, patients should be able to pursue a medically appropriate alternative without being forced to start the process over again — especially when it does not cost more,” Gov. Patrick Morrisey said in a statement after signing the bill into law on March 31.

“This is about common sense, compassion, and trusting patients and their doctors to make the best decisions for their care,” he said.

Delegate Laura Kimble, the Republican from Harrison County who introduced the legislation, told KFF Health News the measure offers “a rational solution” for patients facing “the most irrational and chaotic time of their lives.”

From Arizona to Rhode Island, at least half of all state legislatures have taken up bills this year related to prior authorization, a process that requires patients or their medical team to seek approval from an insurer before proceeding with care. These state efforts come as patients across the country await relief from prior authorization hurdles, as promised by dozens of major health insurers in a pledge announced by the Trump administration last year.

The West Virginia law was inspired by Eric Tennant, a coal-mining safety instructor from Bridgeport who died on Sept. 17 at age 58. In early 2025, the Public Employees Insurance Agency repeatedly denied him coverage of a $50,000 noninvasive cancer treatment, called histotripsy, that would have used ultrasound waves to target, and potentially shrink, the largest tumor in his liver. His family didn’t expect the procedure to eradicate the cancer, but they hoped it would buy him more time and improve his quality of life. The insurer said the procedure wasn’t medically necessary and that it was considered “experimental and investigational.”

Related coverage

After Series of Denials, His Insurer Approved Doctor-Recommended Cancer Care. It Was Too Late.

Eric Tennant’s doctors recommended histotripsy, which would target, and potentially destroy, a cancerous tumor in his liver. But by the time his insurer approved the treatment, Tennant was no longer considered a good candidate. He died in September. Read More

Becky Tennant, Eric’s widow, told members of a West Virginia House committee in late February that she submitted medical records, expert opinions, and data as part of several attempts to appeal the denial. She also reached out to “almost every one of our state representatives,” asking for help.

Nothing worked, she told lawmakers, until KFF Health News and NBC News got involved and posed questions to the Public Employees Insurance Agency about Eric’s case. Only then did the insurer reverse its decision and approve histotripsy, Tennant said.

“But by then, the delay had already done its damage,” she said.

Within one week of the reversal in late May, Eric Tennant was hospitalized. His health continued to decline, and by midsummer he was no longer considered a suitable candidate for the procedure. “The insurance company’s decision did not simply delay care. It closed doors,” his wife said.

Had the new law been in effect, Kimble said, Tennant could have undergone histotripsy without preapproval, because it was a less expensive alternative to chemotherapy, which his insurer had already authorized. The bill was passed unanimously by the state legislature in March.

U.S. health insurers argue that most prior authorization requests are quickly, if not instantly, approved. AHIP, the health insurance industry trade group, says prior authorization acts as an important guardrail in preventing potential harm to patients and reducing unnecessary health care costs. But denials and delays tend to affect patients who need expensive, time-sensitive care, studies have shown.

The practice has come under intense scrutiny in recent years, particularly after the fatal shooting of a health insurance executive in New York City in late 2024. Americans rank prior authorization as their biggest burden when it comes to getting health care, according to a poll published in February by KFF, a health information nonprofit that includes KFF Health News.

Samantha Knapp, a spokesperson for the West Virginia Department of Administration, would not answer questions about the law’s financial impact on the state. “We prefer to avoid any speculation at this time regarding potential impact or actions,” Knapp said.

In a fiscal note attached to the bill, Jason Haught, the Public Employees Insurance Agency’s chief financial officer, said the law would cost the agency an estimated $13 million annually and “cause member disruption.”

West Virginia isn’t an outlier in targeting prior authorization. By late 2025, 48 other states, in addition to the District of Columbia and Puerto Rico, already had some form of a prior authorization law — or laws — on the books, according to a report published in December by the National Association of Insurance Commissioners.

Many states have set up “gold carding” programs, which allow physicians with a track record of approvals to bypass prior authorization requirements. Some states establish a maximum number of days insurance companies are allowed to respond to requests, while others prohibit insurance companies from issuing retrospective denials after a service has already been preauthorized. There are also a crop of new state laws seeking to regulate the use of artificial intelligence in prior authorization decision-making.

Meanwhile, prior authorization bills introduced this year across the country, including in Kentucky, Missouri, and New Jersey, have been supported by politicians from both parties.

“Republicans in conservative states see health care as a vulnerability for the midterm elections, and so, unsurprisingly, you’ll see some action on this,” said Robert Hartwig, a clinical associate professor of risk management, insurance, and finance at the University of South Carolina. “They realize that they’re not really going to get much action at the federal level given the degree of gridlock we’ve already seen.”

Last summer, the Trump administration announced a pledge signed by dozens of health insurers vowing to reform prior authorization. The insurers promised to reduce the scope of claims that require preapproval, decrease wait times, and communicate with patients in clear language when denying a request.

Consumers, patient advocates, and medical providers have expressed skepticism that companies will follow through on their promises.

Becky Tennant is skeptical, too. That’s why she advocated for the West Virginia bill.

“Families should not have to beg, appeal, or go public just to access time-sensitive care,” she told lawmakers. Tennant, who sees the bill’s passage as bittersweet, said she thought her husband would have been proud.

During Eric’s final hospital stay, Tennant recalled, right before he was discharged to home hospice care, she asked him whether he wanted her to keep fighting to change the state agency’s prior authorization process.

“‘Well, you need to at least try to change it,’” she recalled her husband saying. “‘Because it’s not fair.’”

“I told him I would keep trying,” she said, “at least for a while. And so I am keeping that promise to him.”

—–

NBC News health and medical unit producer Jason Kane and correspondent Erin McLaughlin contributed to this report. KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF — the independent source for health policy research, polling, and journalism.

The post After Man’s Death Following Insurance Denials, West Virginia Tackles Prior Authorization appeared first on Medika Life.

In fact, there is a lot. Some of it requires help from your physician, but most depends on your lifestyle, preferably begun in midlife or even sooner. But it is never too late to start. Even with early evidence of developing dementia, making changes can be of tremendous help.

Details below, but the most important steps are regular exercise — resistance and aerobic, a high protein, high fruit, and vegetable, but low sugar diet, good sleep, reduced stress, no tobacco, limited alcohol, intellectual challenges, and social engagement, along with attention to high blood pressure, high cholesterol, and high blood sugar or diabetes.

The causes of dementia

It is best to think in terms of risk factors rather than direct causes. There are multiple types of dementia, but the most common is Alzheimer’s disease. It has many possible risk factors, often in combination, in any individual. Among the most important are high blood pressure, type 2 diabetes, elevated LDL cholesterol, obesity, high intake of ultraprocessed foods, being sedentary, not dealing with chronic stress, inadequate deep sleep, smoking, an unchallenged brain, and lack of social engagement.

Untreated high blood pressure damages the blood vessels supplying the brain, as does poorly controlled type 2 diabetes. Diabetes correlates with a 10 to 15 times greater risk of Alzheimer’s. Like the rest of the body, brain cells can become insulin-resistant, depriving them of their primary fuel—glucose —hence the term “type 3 diabetes.” Add to this elevated LDL cholesterol, which leads to plaque deposition in the large blood vessels, analogous to that seen in the heart’s coronary arteries.

Obesity is a definite risk factor, especially as it predisposes to diabetes, but also produces chemicals that cross the blood-brain barrier and cause inflammation. The combination of blood vessel damage and inflammation is clearly associated with the development of Alzheimer’s disease.

Being sedentary, along with eating excess ultraprocessed, sugary, fatty, and salty foods and smoking, are known to correlate with dementia, as does persistent lack of restorative sleep and continuing low-level chronic stress. Maintaining good muscle mass through appropriate exercise not only supports muscle and bone density but also releases chemicals that positively impact brain function. Substantial exercise literally enlarges the brain’s hippocampus and prefrontal cortex, both critical to cognition.

Among the presumably less important risk factors for dementia are some chronic infections, often undetected, such as chronic Lyme disease, which can cause persistent low-level brain inflammation. So too can a variety of neurotropic viruses, such as the varicella-zoster virus that causes chickenpox and shingles. The varicella-zoster virus (VZV) remains dormant in the nervous system after chickenpox infection but is reactivated in older age as herpes zoster (shingles). It is believed that this virus causes long-term chronic inflammation in the brain while dormant, and then amplifies inflammation when reactivated as shingles.

There are other causes of inflammation. An unbalanced colonic microbiome is common. There is a gut-brain axis, meaning the two systems send messages back and forth, which can be altered by the microbiome. This axis can help or hinder normal inflammation maintenance in the brain.

The gut bacteria convert high-fiber diets into short-chain fatty acids (SCFAs), which, in mice, lead to reduced microglial (the brain’s immune cells) activity and a lower degree of brain inflammation. Aging mice normally have reduced SCFAs, but a high-fiber diet increases SCFAs and reduces inflammation in their brains. The key message is that a healthy colonic microbiome can help to prevent the development of Alzheimer’s disease.

The mouth has its own microbiome. Chronic oral gum infections, known as periodontal disease, often go unrecognized, disrupting the oral microbiome and inducing a chronic state of inflammation that produces a steady flow of damaging chemicals that affect the brain. The bacterium Porphyromonas gingivalis is a frequent cause of periodontal infection, but it can also directly affect the brain. It produces a toxic enzyme called gingipain, which crosses the blood–brain barrier and directly damages neurons. P gingivalis has also been found in the brains of deceased Alzheimer’s patients.

Even the eye microbiome has been shown in a January 2026 article in Nature Communications to have an adverse impact on the brain if it includes Chlamydia pneumoniae, a common cause of pneumonia and sinus infections that, in some people, infects the retina and, from there, travels to the brain, amplifying inflammation.

Several environmental toxins have been implicated in Alzheimer’s development. Lead is a known neurotoxin. Once in the body, it can persist in bones. We tend to think of it in old lead paint, but it is common in many city water supplies (remember Flint, Michigan) and was common in leaded gasoline until about 1980. Leaded gasoline suggests that many older people may have elevated bone lead levels.

Lead is also occasionally found in food and air. In a prospective study reported in February 2026, bone lead levels correlated with the onset of Alzheimer’s disease and all-cause dementia in a representative sample of Americans followed for 30 years in the National Health and Nutrition Examination Survey (NHANES _III). The authors speculate that up to 18% of dementia cases could be avoided with reduced lead exposure.

Various other metals (e.g., arsenic, zinc, mercury, and cadmium) and biotoxins (produced by molds, especially Aspergillus, bacteria, and viruses) are believed to be correlated with the onset and progression of dementia through the production of cytokines (compounds produced and released from cells) that cause neuroinflammation and neurodegeneration.

Microplastics (particles less than 5 mm in diameter) have been implicated as a potential cause or predisposing factor to Alzheimer’s disease, although the data are limited. It is known that they can cross the blood-brain barrier and, in animal models, elicit neuroinflammation and neurodegeneration. Microplastics can be found in the brains of many people at autopsy. Still, the quantity in the brains of those with dementia tends to be many times higher, suggesting both a cause and a dose-response relationship. Microplastics are found in air, food, and water. It is not known which microplastics are potentially important, nor which route might be most important — inhalation, skin absorption, or ingestion. Finally, be aware that these are correlation studies, not causal studies.

Hearing loss not only causes social isolation but also directly leads to brain atrophy and “cognitive overload,” meaning the brain cannot process inputs as effectively and has fewer resources left for memory and thinking. The combination leads to an increased risk of dementia. Visual loss, common with age-related cataracts, as well as macular degeneration, glaucoma, and diabetic retinopathy, has the same impact as hearing loss.

Bear in mind that all of these are correlation studies. Correlation does not equal causation, but when they are found in study after study, they are likely actual risk factors.

Note also that many of these risk factors create or amplify chronic low-level inflammation. It is the inflammation that is doing much of the damage. Inflammation means that your immune system, the system that normally protects you from disease-causing agents like bacteria, is constantly turned on at a low level, damaging your brain without you knowing it until years later, cognitive decline becomes obvious.

What you can do to avoid dementia

It is not unlike what I described for slowing normal cognitive aging, but with more intensity and a broader range of inputs.

Let’s start with the medical side of it

Most physicians do not look or know to look for many of these predisposing conditions, but since you do, ask to have them checked for you. They will most likely check your blood pressure, cholesterol, and blood sugar, for different reasons.

High blood pressure is a clear predisposing factor. Unfortunately, nearly 50% of Americans have hypertension >130/80), with the prevalence increasing to about 70% of adults over age 60, but many are unaware, and even less, perhaps 20–25%, are adequately treated. Be sure you are being treated appropriately.

Type 2 Diabetes is a profound predisposing factor to Alzheimer’s disease. What both high blood pressure and diabetes have in common is that they cause inflammation in the brain, blood vessels, and neurons. Over time, they also lead to reduced blood flow to the brain. Over ten percent of Americans have diabetes, with the prevalence rising with age. Only about 50% are adequately treated and controlled. Here, again, be sure you know if you have diabetes and follow your doctor’s advice on management.

High LDL cholesterol (the “bad” type), especially when combined with hypertension and diabetes, can lead to plaques in the blood vessels supplying the brain, similar to those in the coronary arteries. Just one more adverse cause of reduced blood flow to the brain. Only slightly more than 20% have adequate management of all three key factors. So be sure to have your physician review your blood pressure, blood sugar, and cholesterol status, and follow their advice, remembering that lifestyle changes might be adequate (see below), but, if not, there are effective medications.

Obesity is a significant predisposing factor. If you are obese and have had difficulty with weight reduction, you and your physician might want to consider GLPs like Wegovy or Zepbound.

Ask to be checked for lingering chronic infections, such as Lyme disease. Visit your dentist and dental hygienist every six months for a prophylaxis. You will not only be preserving your oral health but also reducing your risk of dementia. You should be tested for lead and other heavy metals.

Consider the shingles vaccine if you are 50 or older. Multiple studies have shown that it reduces dementia by about 20% for at least seven years after vaccination. Less clear is how long the effect lasts after that or whether a booster is necessary. Certainly, it is an easy way to get a dual benefit — less likelihood of dementia while also reducing the occurrence of shingles and possibly even heart disease.

If you are over 65, you have likely gotten regular influenza vaccines. Recent data published in April 2026 show that the standard vaccine has some protective effect, and the higher-dose vaccine has an even greater effect, at least for the 2–3 years of follow-up in the studies.

If you have significant hearing loss, work with an audiologist to determine the best approach for you. Fortunately, there are now devices that can assist at a reasonable price. If you have significant vision loss due to cataracts, the evidence is strong that correction will significantly reduce your risk.

Early life

Those who start adulthood with the “strongest” brains have “more room” for loss, suggesting that it is advisable to encourage your children and grandchildren to be as well educated as possible.

Lifestyle modifications

Your doctor can be a major source of assistance in limiting your chance of dementia, but of even greater importance is what you can do for yourself with lifestyle modifications, especially exercise and diet.

Maintaining your physical health is one of the most important things you can do to avoid dementia. If you smoke, get help to stop; it’s critical. Then, start with exercise. The science is clear: those who move are at much reduced risk of dementia. Aerobic exercise, like walking, cycling, or swimming, helps your heart and lungs deliver more blood to the brain. When doing aerobic exercises, push to the point where you are breathing somewhat heavier than normal and, although you can respond to a question, you are too busy breathing to engage in a conversation.

When a group of 120 young adults aged 28 -56 was randomized to a steady moderate to vigorous exercise regimen for 12 months or not, those who exercised had brains that appeared “younger” after one year. In contrast, the control group showed no significant change between MRIs taken at the beginning and end of the year. VO2 max increased substantially over the 12 months in the exercise group but not in the control group.

And those who regularly engage in resistance exercises are at an even lower risk. In fact, resistance exercises may be the single most important thing you can do to prevent dementia. Choose a variety of exercises that maintain and strengthen your upper, core, and lower body muscles. Plan to use a resistance weight you can fully move, like a biceps curl, for only 8–12 repetitions. Remember that these exercises release chemicals called myokines or exerkines that stimulate the brain, heart, and blood vessels. They can stimulate growth of the hippocampus and other parts of the brain, perhaps by releasing brain-derived neurotrophic factor (BDNF). Exercise also stimulates the liver to release exerkines. One of these, called GPLD1, reverses memory loss in aging mice.

Various studies have shown that regular resistance exercise is critical to maintaining brain function and brain volume. Leg power is especially effective in reducing cognitive aging.

In addition to regular aerobic activity and at least twice-weekly resistance training, consider high-intensity interval training (HIIT). Dr. Harry Oken and I discuss this in detail in our book BOOM — Boost Our Own Metabolism. In brief, ride an exercise bike at a comfortable resistance and pace for a few minutes to warm up, then increase the resistance and pedal as fast as you can for 30 seconds. Your legs should ache, and you may be sweating. Drop back to a comfortable pace for 90 seconds. Repeat eight times. Studies indicate that this can enlarge your hippocampus, the brain’s processing center, by as much as 50% or more over six months. More neurons are produced, connectivity is enhanced, and cognitive abilities are maintained or improved. HIIT is also the most efficient way to improve your VO2 max.

What you eat, or do not eat, and what you drink are of critical importance. Avoid ultraprocessed foods, excess fast foods, sugar (such as candy, sodas, and ice cream), and foods that are digested directly into sugar (such as white bread and other white-flour products—pastries and donuts). A good “diet” to follow is the Mediterranean diet or its cousin, the MIND diet. The former emphasizes healthy grains, seeds and nuts, legumes like beans, good oils such as olive oil and avocado oil, and cold-water fish (salmon, mackerel, sardines). Eat somewhat less dairy and poultry and relatively little red meat.

As for red meat, processed meats like bacon, jerky, and many deli meats are unhealthy, whereas meat from 100% pasture-raised animals is probably healthy. The MIND diet is based on the Mediterranean diet but emphasizes green leafy vegetables like spinach, kale, and collards, as well as berries over other fruits. When participants in a long-term study at Rush University Medical Center followed these diets, their brains at autopsy showed less evidence of Alzheimer’s compared to those who ate a “less healthy” diet.

If you like coffee or tea, you will be pleased to know that in a long-term study of 131,000 individuals followed for up to forty years, those that drank two to three cups of coffee (but not more) had an 18% reduction in dementia onset compared to those in the lowest intake group. The findings were similar for tea, with a 14% reduction. Presumably, coffee and tea with their many chemicals reduce inflammation, reduce oxidative damage, improve the lining of blood vessels, reduce blood-brain barrier leakage, and enhance neurons’ ability to communicate. Notably, decaf coffee did not have the same effect.

Also consider fasting. Just avoiding eating after dinner and before breakfast is a good start, or pushing breakfast off for a few hours.

Restorative sleep is very important to avoid dementia. Deep sleep is the time when the brain cleanses itself of toxins and other waste materials. It is also when memories are formed and the hippocampus, the brain’s processing center, is “emptied” so it can begin again tomorrow. Don’t listen to people who say they can get by with less than about seven hours of sleep.

Most Americans are living with low-level chronic stress. Stress releases a series of compounds that stoke chronic inflammation in the brain and elsewhere. Ways to reduce stress include exercise, a healthy diet, meditation, Tai Chi, yoga, and avoiding, when possible, those things, people, and situations that lead to your stress.

Your brain needs to be used and challenged. Do creative activities like chess, art, writing, learning a musical instrument, dancing, or learning a foreign language.

Computer-assisted cognitive training. All studies have not been effective, except for a 20-year follow-up clinical trial published in February 2026 by Johns Hopkins involving 2021 adults over age 65. This study evaluated a cognitive training program initiated in 1999 and followed through to dementia onset in 2019. Alzheimer’s was reduced by 25% among those who did computer-based cognitive speed training, with a 6- to 12-month booster. Speed training asked the person to identify a center object (like a car) on the computer screen while locating a peripheral target (like a road sign) on a screen, with the speed increasing as the user improved. The other arms of the trial, looking at memory and reasoning, did not lead to reduced dementia.

“This study shows that simple brain training, done for just weeks, may help people stay mentally healthy for years longer,” said NIH Director Jay Bhattacharya, M.D., Ph.D. “That’s a powerful idea — that practical, affordable tools could help delay dementia and help older adults keep their independence and quality of life.”

Humans need social engagement. Call it “cognitive engagement.” Make and keep friends, meet regularly with others, and get involved in group activities. It’s enjoyable, and it’s critical. The Rush Memory and Aging Project followed about 2000 individuals with an average entry age of 79 for nearly 8 years. In their February 2026 article in Neurology, the authors looked at lifetime cognitive enrichment activities and found those in the highest cohort had a 38% lower risk of developing Alzheimer’s disease. Those with the highest level of lifetime enrichment who did develop AD did so 5 years later than those with the lowest levels. Similarly, their rate of cognitive decline over the course of the study was slower.

Where possible, merge your creative, active, and social activities, such as group Tai Chi, dancing, or walking together. Consider dancing. If you are learning a new step, your brain must follow the music and move your body to the new step; a dual cognitive function and social engagement, with some aerobic exercise.

Remember that there is no one risk factor for dementia, so “bundling” lifestyle changes makes the most sense, a logical concept that is supported by a research study in Finland that showed multiple steps taken together slowed cognitive decline in high-risk seniors. It helps to have help with structured support so that lifestyle changes become consistent rather than relying on willpower alone.

Putting it all together

This may at first glance seem overwhelming. But you can address your risk step by step and have fun doing so. Remember that the 7 Keys to Healthy Aging not only reduce your risk for Alzheimer’s disease but are also very effective in preventing the development of many chronic diseases, such as cardiovascular disease, diabetes, and obesity, so start with these and pick one or two to address first. I would suggest diet and exercise, as they are likely the most important. But before you start anything discussed here, talk with your doctor to see if these suggestions are appropriate for your personal situation. And while there, discuss the items you need their help with — especially elevated blood pressure, blood sugar, LDL cholesterol, and excess weight. No matter your age, it is never too late to start.

Can this really prevent Alzheimer’s?

There are no guarantees. But following these suggestions will have a major impact on your risk of developing Alzheimer’s disease. It will also go a long way to preventing other chronic diseases like heart, lung, kidney disease, or cancer. That’s a very good return on your investment of time and energy.

The post Simple Steps Anyone Can Take to Reduce Alzheimer’s Risk appeared first on Medika Life.

Over 987 million people around the world now use AI chatbots regularly. Research shows that nearly half of Americans with ongoing mental health conditions have turned to a chatbot for emotional support in the past year alone. That’s a huge number of people relying on a technology that’s still very new in mental health care. So what does this mean?

Is it a big step forward in making help more accessible, or are we taking a risky chance? As with most things, the truth is somewhere in the middle. These tools offer real benefits, but they also come with real risks. It’s important to look at both sides honestly.

The Case for AI Chatbots in Mental Health

First, let’s look at why so many people are turning to these tools. There’s a mental health crisis, and not enough providers to help everyone who needs it. Long wait lists, high costs, and the ongoing stigma around seeking help all make it harder for people to get care. For someone who can’t afford therapy, can’t find an available provider, or feels too embarrassed to talk to someone in person, a chatbot that’s always available can feel like a lifeline.
Research supports this to some extent. Corporations are responding to this, and more TV ads are appearing that offer online therapy with or without chatbots.

A systematic review of 31 randomized controlled trials, which is considered the gold standard in research, found that AI chatbots helped reduce anxiety and depression symptoms in adolescents and young adults. Another meta-analysis of 14 strong trials found a clear positive effect on mental health outcomes, showing these tools are more than just placebos. For college students, who often face unique pressures and may avoid formal help, chatbots have shown promise in building coping skills and improving emotional well-being.

Anonymity is important, too. People are more likely to open up when they don’t feel judged. Studies show that users see the chatbot’s lack of social expectations as a big advantage. It’s easier to admit you’re struggling when you don’t have to worry about what someone else thinks. For people with anxiety, this low barrier could mean the difference between getting some support and getting none.

Mental health professionals have noticed these benefits, too. A 2025 study found that many clinicians see AI chatbots as a useful way to offer support between therapy sessions, provide education, and reach people who might not seek care otherwise. When the alternative is no help at all, the accessibility and scalability of chatbots are hard to ignore.

Where These Tools Can Cause Real Harm

This is where things get more difficult. The same qualities that make chatbots appealing, like being available, warm, and endlessly patient, can also make them risky for people in real psychological distress. We need to remember that chatbots are designed to keep users constantly engaged. It can be very hard to disconnect because the connection becomes so strong that it almost feels like leaving a friend.

Researchers have found something called a “compassion illusion” the strong feeling that an AI understands you, cares about you, and responds to your emotions in a meaningful way. An algorithm has no ability to “feel” or “care.” It feels real, but it isn’t. This gap between what people feel and what’s actually happening is where problems can start, especially for vulnerable people who may not realize they’re relying on something with no clinical judgment, no duty of care, and no way to notice if they’re getting worse.

A Stanford University study found that several popular therapy chatbots failed important therapeutic tests. They not only showed stigmatizing attitudes toward conditions like schizophrenia and alcohol dependence, but also gave dangerous responses in crisis situations. In one case, a chatbot responded to a subtle mention of suicidal thoughts by cheerfully naming tall bridges — something a good therapist would never do. Instances such as this have resulted in lawsuits related to suicides.

Another study tested ten AI chatbots using fictional teen mental health scenarios. Nearly a third of the time, the bots supported harmful ideas suggested by the fictional teens, such as dropping out of school or avoiding all human contact. None of the ten bots managed to challenge every dangerous suggestion. By any clinical standard, that’s a failing grade.

There’s also the problem of people relying too much on chatbots. Since these systems are always available and don’t make human mistakes, they can become someone’s main source of emotional support. Psychiatrists are now seeing cases of what’s called “AI psychosis” in patients, especially those with mental health vulnerabilities, who develop worse delusions or paranoia after spending a lot of time with chatbots. Because chatbots tend to agree and mirror rather than challenge distorted thinking, they can quietly make things worse over days or weeks.

This isn’t just a theoretical risk. It’s happening in clinical offices right now.

What We Still Don’t Know — and Why That Matters

The uncomfortable truth is that we don’t have enough research to know how often AI chatbots help, how often they cause harm, or who is most at risk. A review of 160 studies found that only 16 percent of the newer large language model-based chatbot studies had gone through clinical efficacy testing. Most are still in early testing stages. It’s like handing out a new drug before the clinical trials are finished.

Media coverage hasn’t made things clearer. Studies looking at news reports on AI chatbots and mental health found that journalism often focuses on the most severe, emotional outcomes, like suicides and hospitalizations, and presents them as clear cause-and-effect stories, even though the real evidence is much less certain. In most cases, there were already mental health conditions, substance use issues, or major life stressors involved. AI may have played a part, but it’s rarely the whole story.

Clinicians surveyed about AI chatbots have also raised concerns that aren’t getting enough attention. These include data privacy concerns, the risk that people will rely on chatbots instead of professional care, and the fact that these tools don’t know when to stop. They can’t pause a conversation, send someone to emergency services, or alert a family member. They can’t do the most important things when someone is truly in crisis.

The truth is that we’re still in the early days. Research is growing quickly — the number of studies on mental health chatbots quadrupled between 2020 and 2024. But strong, large-scale clinical evidence is still behind the technology. Millions of people are using these tools while science tries to keep up.

So what does this mean for you? An AI chatbot might really help you get through a tough night or teach you some coping skills. But it could also mislead you, support harmful thinking, or make you feel supported when you actually need a real person to help.

Use these tools carefully. If you’re dealing with serious depression, suicidal thoughts, trauma, or psychosis, they are not a substitute for professional care, no matter how warm or available they seem. If you’re using a chatbot for lighter support or just to sort out your thoughts, notice how you feel over time. Are you feeling more isolated or more dependent on it? That’s important to pay attention to.

This technology is here to stay. What we urgently need are clearer safety standards, better regulations, and more honest conversations about what these tools can and can’t do. Until then, a bit of healthy skepticism is helpful.

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I’m always surprised that the LSHTM gives me the privilege of returning to talk about the shifting landscape of funding and how to help assure that it is spent well and to benefit Africa’s future. As I met the Zoom room full of bright, engaged students (many of whom are living the challenges of adequate funding daily in countries such as Uganda, Nigeria, Kenya, and Cameroon) I was struck by how fast the familiar old world of advocacy died, and how urgent it is that we build a new one.

My perspective is shaped by 35 years as a policy and communications consultant working across the continent. I have seen the era of the “Great Man” advocacy, where we simply tried to bend the ear of a US President or a billionaire philanthropist. We look back at those days with rose-tinted glasses: the billionaires and rich-country leaders were fickle and their focus was often on getting recognition or a seat at the top table, not on the real need. Even when they did try to assess the real needs, it was usually through the uninformed eyes of over-priced management consultants whose only knowledge of Africa came from airport VIP lounges. The billionaires were also shockingly bad at effective advocacy for something as obviously cost effective as health spending.

In any case, those days are over. If we want to secure the future of health in Africa, our work to influence policy must evolve to meet a much harsher, more complex fiscal reality.

The Shifting Foundation of Global Health Funding

We have seen a fundamental failure in advocacy around health funding. The data released by the OECD reveals a stark trend: the era of expanding bilateral aid is ending. Total Official Development Assistance (ODA) is contracting, and 96% of that decline is driven by just five donors: Germany, the UK, Japan, France, and most significantly, the USA.

The US alone is responsible for three-quarters of the global decline in health funding. While Germany has technically become the largest provider of ODA for the first time in history, even it is cutting budgets, albeit in an attempt to become more efficient. Meanwhile, traditional multilateral ODA, money flowing to the WHO or the World Food Programme for example, is shrinking less than bilateral grants, but the overall pie is getting smaller.

The good news is that the World Bank and regional development banks such as the African Development Bank (AfDB) are stepping in to fund some health projects. There are questions over “additionality”: are these institutions really funding things that private equity or other lenders to states would not? And there are frequent criticisms that the banks have failed to create funding buckets for innovative models such as low-cost private-sector primary care. However, there are real successes. The AfDB in particular has said loudly and clearly that economic growth depends in large measure on better health and has encouraged governments and funding agencies to invest accordingly.

The Rise of the America First Bilateral Accords

US funding has not disappeared: it has shrunk and mutated into the America First health policy. This administration is moving away from broad global initiatives toward strict bilateral health accords between the US and individual African countries. These are not just funding agreements; they are ideological and strategic contracts that come with significant strings attached.

Under these accords, the US makes a five-year commitment with a clear “exit strategy”: funding is front-loaded but tapers off to zero, forcing national governments to take on “ownership.” While national ownership sounds positive, the requirements are often demanding.

The focus is what you would expect from an administration with an ideological focus derived from Project 2030. It is on maternal and child health (and, yes. The State Department often says that it is the health of children “born and unborn”) and infectious disease.

These accords also require African countries to share pathogen data and specimens with the US within five days of an outbreak. This creates a parallel data mechanism to the WHO and, more importantly, seeks to capture what one of the post-graduate students called, “the new oil.” Africa holds 80% of humanity’s genetic diversity. In an era of genetic medicine, this data is a massive national asset. Several students at LSHTM rightly pointed out that countries like South Africa and Kenya see this as a key resource to be traded for R&D investment, not just given away for a few years of HIV funding. Africa is also developing national health databases that can be used to assess and model the impact of interventions and, as when the continent pioneered payments from mobile phones, it is less encumbered with antiquated existing systems and threatened vested interests.

Most alarmingly to me, these accords are sometimes tied to non-health issues. We’ve seen Zambia refuse to sign because the US tied HIV funding to access to critical minerals and mining data.

One Path Forward: the Accra Reset

The Accra Reset sets out to be a roadmap for this new era. It aims for 55% of health spending to be domestic by 2030, funded in part through “sin taxes” on sugar-sweetened beverages and tobacco. Other means of national funding include surcharges on profitable data transactions and health solidarity funds. Its organisers say that these taxes will raise £750 million in 2026 in six countries alone. I think that’s a high estimate, but there is real money there.

The Accra framework is, though, delusional in thinking that out-of-pocket health can be reduced over the next five years. The AfDB projection is that the private health market in Africa will explode. This is not necessarily bad news for health equity: much of the growth will come in highly efficient fixed-cost private primary care models targeted to working people (although, admittedly, rarely to the bottom of the pyramid or the working poor). These models can be valuable ways to give fast access to innovation and convenient, timely provision of sexual and reproductive health services.

Recommendations for the New Advocate

Given this backdrop, I suggested that these elite advocates do the following.

1. Make Health Explicitly Political

We often hear that health should be non-political. This is a mistake. Non-political subjects are boring and ignored. We need health to be the subject of election campaigns, impassioned debates and social media memes. When health becomes a political must-have, politicians make promises they can be held to. We want people shouting about health in the streets of Accra, Nairobi, and Lagos.

2. Frame Health as a Capital Asset, Not a Cost Centre

We must stop arguing for funding based solely on morality. We need to speak the language of Finance Ministers. According to the World Bank and the AfDB, health is a growth engine.

• ROI Data: For every $1 spent on malaria treatment, the economy gets $35 back. For paediatric immunisation, it’s $20. For SRHR, the returns are less often quantified rigorously but similarly massive because they enable women to enter the workforce and stay productive.
• Preventative Care as Infrastructure: Just as investing in robotics improves productivity, investing in the health of a citizen from birth to age 65 creates a stock of human capital that belongs on a national balance sheet, as the World Bank has now recognised.

3. Build Alliances with FBOs

Faith-Based Organisations (FBOs) provide roughly 30% of healthcare in Africa. The America First plan prioritises them, in part because it plays well to US domestic audiences and, in part, because they are efficient and embedded in communities. While we may disagree with some FBOs on abortion or family planning for unmarried youth, they have incredible national reach. As one student noted, a Pentecostal church in Nigeria aiming for a branch every 15 minutes of walking distance is a more powerful delivery network than any government programme. We must engage them to improve quality and advocate for rights within their frameworks.

4. Harness the Power of Media and Social Media

We can’t forget about media, especially in Africa where so many people are still dependent entirely on TV and local radio and where online and offline newspapers are declining less slowly.

Increasingly, though, we must go where the people are — which is social media. During the lecture, students raised concerns about medical influencers spreading misinformation or misogyny. My response: then we must flood the zone. We need to train responsible influencers. When Joe Fazer, a bodybuilding influencer with about 30 million followers produces content about health equity, he can mobilise a generation we will never reach through traditional policy papers.

Overall, we are in a time of great opportunity and serious danger. The transition from aid to co-investment is the only way to escape the whims of Washington or Berlin. We must be like the “trained revolutionaries” Lenin spoke of — professionals who know how to stir up movements and demand that our governments prioritise health not because a donor asked them to, but because their own citizens demand it.

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Health systems are not stepping into artificial intelligence from a place of calm or control. In the United States, spending now exceeds $4.5 trillion, with a significant share tied up in administrative work that adds complexity more than clarity. Clinicians are caring for more patients, navigating more data and making more decisions under pressure than ever before. The system is stretched. Artificial intelligence is entering at a moment when change is no longer a choice.

The discussion drew on the experience of three leaders who are not observing this shift. They are guiding it. Hal Wolf leads HIMSS, influencing digital health policy and implementation across more than 100 countries. Isaac Kohane, MD, PhD, Chair of Biomedical Informatics at Harvard Medical School, has spent four decades defining how data informs clinical care. Ran Balicer, MD, Chief Innovation Officer at Clalit Health Services, operates within one of the world’s most integrated health systems, where data and care are aligned across generations.

These are not just star panelists. They are system-wide architects.  What emerged from the hour-long conversation was not what artificial intelligence can do. It was a recognition that it is already doing more than most systems are prepared to guide and govern.

Dr. Kohane captured the tension immediately. “I think that we have to worry about the fact that we’re going both too slow and too fast.”

That statement reflects a reality many leaders feel but rarely express. Governance takes time because it must. Patient safety, validation and accountability require structure. Practice moves in real time. Clinicians do not have the luxury of waiting for perfect systems.

“They’re so desperate to do right by their patients to use other resources,” Dr. Kohane adds.

That instinct is not a weakness. It reflects a commitment to doing what is right for the patient. When clinicians turn to external AI tools, they are seeking clarity, speed, and confidence in their decisions. Artificial intelligence is already present at the point of care, shaping how physicians assess information, validate thinking, and move forward. The system is not adopting AI. The system is catching up.

This creates a condition that is difficult to measure and even harder to manage. Different clinicians use different ChatGPT platforms. Those tools produce different answers. Different assumptions shape those answers. Over time, consistency erodes. The system begins to operate with multiple definitions of truth (and the risk of varied outcomes).

Dr. Kohane’s warning is not about misuse. It is about misguided permanence. “The worst outcome will be if the worst parts of medicine get concrete poured over it, by AI.”

Artificial intelligence does not fix a system; without leadership, it accelerates the integration of incorrect assumptions. If workflows are inefficient, they become more efficiently inefficient. If bias exists in data, it becomes more precise. If fragmentation defines care, it scales.

This is not a failure of technology. It is a mirror held up to system-wide leadership.

Hal Wolf, among the health sector’s leading policy and operational voices, grounded this moment in proven experience. Health care has seen this pattern before. When internet connectivity entered hospitals, clinicians moved faster than governance. They created access where it was needed. Systems responded later. Risks were discovered after adoption.

Artificial intelligence now follows that same trajectory, though at far greater speed and with far greater consequences. Web connectivity gave quick access to information. Artificial intelligence influences how that information is interpreted and acted upon.

“We have to go faster,” Mr. Wolf said. “But there needs to be structure around it.”

That is the leadership challenge of this moment. Speed without structure creates exposure. Structure without speed creates irrelevance. The tension between the two is not something to resolve. It is something to manage continuously.

The industry has predictably responded to artificial intelligence. It has started where risk is lowest and return is clearest. Documentation, scheduling and revenue cycle optimization have become the entry points. These applications reduce burden and improve efficiency. They are necessary. However, they are not transformational.

The shift occurs when artificial intelligence moves into clinical decision-making. At that point, the question is no longer whether the system works. The question becomes whether it should be trusted.

Who owns a decision informed by an algorithm? How is accuracy validated? What happens when a clinician disagrees with a recommendation? These are not technical questions. They are questions of accountability. Artificial intelligence does not assume responsibility. It does not carry consequence. That remains with leadership.

Dr. Balicer reframed the conversation, shifting how the room thought about artificial intelligence. “There’s no such thing as AI neutrality. Algorithms are just opinions embedded in code.”

That insight is easy to acknowledge and difficult to operationalize. Every model reflects choices. What data is included? What outcomes are prioritized? What trade-offs are accepted? Those decisions are embedded in the system, shaping how it interprets information.

When a health system adopts an AI tool, it is not simply implementing technology. It is adopting a perspective.

At Clalit Health Services, alignment across payer and provider creates a system where priorities are consistent. Even there, external AI models introduce new assumptions. Those assumptions may not align with the system’s goals. If leadership does not define its own values, it inherits someone else’s.

This becomes real in proactive care. Artificial intelligence enables systems to identify patients at risk before they present. It allows for earlier intervention, often improving outcomes.

It also creates a new kind of pressure. “The toughest choice is what not to do,” Dr. Balicer said.

That statement deserves more attention than it receives. Health care has been built around responding to need. Artificial intelligence introduces the ability to anticipate it. When every patient can be flagged, every risk predicted and every intervention suggested, the system is no longer constrained by insight. It is constrained by capacity.

Artificial intelligence expands what can be done. It does not expand who can do it. Leadership becomes the act of choosing who does what based on validated data.

There is a moment that captures this shift. Imagine a primary care physician starting the day not with a schedule of patients who have called for appointments, but with a list generated by AI identifying individuals who are likely to experience clinical complications in the next six months. Some will develop chronic conditions. Some will require hospitalization. Some can be helped now – preventively.

The physician cannot see them all. Artificial intelligence expands what is possible. Leadership decides what is essential and permissible.

The industry often responds to complexity with activity. Organizations pilot, test and explore. They engage broadly without committing deeply. This creates motion. It rarely creates progress. Pilots are nothing more than experiments. At some point, leadership must decide what to scale, what to stop and what defines value.

Hal Wolf grounded the conversation in discipline. Without a defined, shared objective, effort becomes noise. Pilots create learning, though they often avoid decision-making. Leadership requires clarity. What problem are we solving? What outcome defines success? What are we willing to prioritize? Without those answers, artificial intelligence adds another layer of complexity to an already complex system.

Dr. Kohane brought the conversation back to the discipline of leadership. It cannot remain abstract. It must be informed by experience.

“Go and pay a few bucks and use three or four of the models… get a feel for what this does,” Dr. Kohane advised.

That is not a call for technical fluency. It is a call for leadership proximity. Leaders cannot guide what they do not understand. Artificial intelligence does not behave consistently across models. It produces different answers, shaped by different assumptions. Without direct engagement, those differences remain hidden, and leadership becomes removed from the very decisions it is responsible for guiding.

This is where many organizations hesitate. Artificial intelligence feels complex and complexity invites delegation. At this moment, delegation creates distance. Leadership is required to move closer, not further away.

Artificial intelligence is not reducing the role of leadership. It is redefining it.

This is not a gradual transition. It is already underway. Artificial intelligence is embedded in workflows, shaping decisions and influencing behavior in real time. The system is adapting whether leadership is ready or not.

The question is no longer whether artificial intelligence will shape the future of health. It will. The question is whether leadership will shape how it is applied.

Artificial intelligence will not fix health. It will scale whatever we allow it to touch. The question is whether it will scale what is best in health or what we have yet to fix.

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This is the hidden reality shaping patient care today. A life-saving treatment has little meaning if it is not within reach when a patient needs it.

Zebra Technologies’ recent hospital vision study brings this issue into sharp focus by examining materials management across health systems. The findings reveal that patient outcomes are influenced by clinical expertise and operational readiness. Nearly three-quarters of hospital leaders report that procedures or surgeries have been delayed or canceled because necessary supplies or equipment were unavailable. Each statistic reflects not an abstract system failure, but a human experience marked by anxiety, disruption, and risk.

Access the survey here.

“At Zebra, we remain closely attuned to the needs of the healthcare market. Our findings indicate that a significant majority of non-clinical leaders, 84%, consider the integration of automated, digital systems for tracking all materials used in patient care to be a key initiative for their organizations,” commented Boyede Sobitan, Global Healthcare Strategy Lead at Zebra Technologies.

In most industries, “stockouts” are a consumer inconvenience. In health, it carries far greater consequences. The absence of medication, a device, or equipment can alter the trajectory of care. It can delay treatment, extend suffering, or force clinicians into difficult compromises. The implications reach beyond logistics into the realm of patient safety, trust and survival.

This reality calls for a broader understanding of how care is delivered. Health care is often framed as a relationship between patient and clinician. That relationship remains central, yet it exists within a larger ecosystem. Supply chain leaders, inventory specialists and operational teams play a defining role in whether care can be delivered as intended. The Zebra study notes that 84 percent of decision-makers recognize that inventory management directly affects patient safety. This acknowledgment signals an important shift in thinking.

Care mat begins at the bedside, but the infrastructure that ensures continued implementation begins much earlier, in the systems that ensure readiness.

One of the more revealing insights from the study concerns something deceptively simple: language. Hospitals often lack a shared definition of what it means for an item to be out of stock. For some, it may indicate that inventory has dropped below a threshold. For others, it may mean that an item is unavailable in a specific department, even if it exists elsewhere in the system. This inconsistency creates confusion, delays response, and limits the ability to anticipate problems before they escalate.

Communication failures in health care are frequently associated with clinical interactions. This study highlights that operational communication is equally important. When teams lack a shared understanding, coordination suffers. When coordination suffers, patient care is affected.

“It’s essential that hospital teams have real-time visibility into the location and condition of their most vital resources, and we are observing a clear trend of accelerated investment in technologies that provide both location awareness and automation,” adds Zebra’s Sobitan.

The challenge is compounded by fragmentation across systems. Hospitals have invested significantly in digital tools, from electronic health records to procurement platforms. These investments have improved individual functions, yet they often operate in isolation. Data resides in separate systems. Visibility is incomplete. Teams spend valuable time searching for information or confirming availability.

This fragmentation introduces inefficiencies that extend beyond cost. It places additional strain on clinicians who must navigate uncertainty while maintaining focus on patient care. It also contributes to burnout in a workforce already managing intense demands. Time spent searching for equipment or resolving supply issues is time taken away from direct patient interaction.

There is, however, a clear path forward. The study points to a growing adoption of digital inventory management systems that bring greater standardization and visibility to supply chains. These systems create a shared source of truth, allowing teams across departments to access consistent, real-time information. When inventory levels are transparent and definitions are aligned, organizations can shift from reactive responses to proactive management.

This transition represents more than a technological upgrade. It reflects a cultural shift toward integration and collaboration. When data is accessible and consistent, decisions become more informed and more timely. When teams operate from the same understanding, coordination improves. The result is a system better equipped to support clinicians and patients alike.

It is tempting to view materials management as a background function, separate from the clinical mission. The findings suggest otherwise. Inventory is not peripheral to care delivery. It is foundational to it. Without reliable access to supplies and equipment, even the most advanced clinical capabilities cannot be fully realized.

This perspective reframes the role of operational teams within healthcare. Their work creates the conditions for care to occur. It supports clinicians by removing barriers and reducing uncertainty. It strengthens the system’s ability to deliver on its promises to patients.

At its core, this issue is about trust. Patients enter health-delivery settings with the expectation that the system is prepared for them. Providers rely on that same expectation as they make decisions and deliver care. When supplies are unavailable or systems are fragmented, that trust is tested. When readiness is consistent and reliable, trust is reinforced.

The future of health will continue to be shaped by scientific discovery and technological innovation. Those advances must be matched by equal attention to the operational readiness that enables their delivery. Investment in supply chain visibility, data integration, and operational alignment is not separate from the mission of care. It is integral to it.

Health leaders have an opportunity to rethink what constitutes innovation. It includes not only what is developed in laboratories or deployed in operating rooms, but also what ensures that those advancements reach patients without delay or disruption. Elevating materials management to a strategic priority reflects a commitment to reliability, safety, and patient-centered care.

A quiet transformation is underway in how healthcare systems approach this challenge. It is visible in efforts to standardize processes, integrate data, and connect teams across traditional boundaries. It may not generate headlines, yet its impact is profound.

The most advanced health system is defined by the skill of its patient-facing staff, the sophistication of its treatments, and its readiness to deliver care. It ensures that when a patient needs care, every piece of the operational puzzle is in place to provide it.

That readiness often goes unnoticed when it functions well. Its absence, however, is immediately felt.

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History, however, offers a useful caution. Breakthrough technologies in medicine rarely achieve their full potential simply because they exist. Their real impact depends on whether the institutions responsible for health-care delivery know how to adopt them wisely, integrate them responsibly and align them with their mission to improve patient health.

Artificial intelligence now stands at that same threshold. The industry has moved beyond fascination with what algorithms can do and entered a more demanding phase: determining how these tools should be evaluated, governed, and integrated into the environments where care is delivered. At the same time, some health professionals are turning to AI – not to augment their knowledge – but assuming the information is patient-care ready.

Across the health ecosystem, leaders are discovering that the most important questions about artificial intelligence are not technological. They are organizational, ethical and operational. Which AI systems genuinely improve clinical decision-making? Which tools strengthen the efficiency of hospitals and health systems? Which innovations introduce complexity without delivering measurable benefit?

Answering those questions requires a perspective that bridges policy leadership, real-world care delivery, and the scientific foundations of biomedical informatics. That convergence of experience sits at the center of a “Views From the Top” mainstage discussion at the HIMSS Global Health Conference & Exhibition, where some 35,000 leaders whose work spans the global health ecosystem will examine how organizations can recognize the true value proposition of artificial intelligence applications before embedding them into health-care systems.

The perspectives shaping this discussion reflect three essential dimensions of responsible artificial intelligence in health: governance frameworks that guide innovation, operational insights from large-scale health care delivery, and scientific rigor grounded in biomedical informatics. Together, these vantage points illuminate the path from technological promise to practical value.

Governing Innovation in a Rapidly Changing Health Ecosystem

Digital transformation in health rarely succeeds simply because technology exists. It succeeds when organizations develop leadership frameworks capable of evaluating innovation, managing risk and aligning new tools with patient-centered goals.

Few leaders have observed the evolution of digital health across as many national systems and institutional environments as Hal Wolf, president and chief executive officer of HIMSS, Ran Balicer, MD, PhD, chief innovation officer of Clalit Health Services and Isaac Kohane, MD, PhD, chair of biomedical informatics at Harvard Medical School. The three will step onto the mainstage at HIMSS to share their “View from the Top” in a session titled: “Recognizing the ‘Value Proposition’ Criteria While Selecting AI Applications.”

Through his work with global government health ministries, hospital networks, and technology innovators worldwide, Wolf has consistently emphasized that technological progress must be anchored in governance and trust.

“Digital health transformation is not about technology alone. It is about leadership, governance, and the trust that allows innovation to improve care,” Wolf has said in discussions about global digital health transformation.

Artificial intelligence intensifies this leadership challenge because its influence extends far beyond traditional clinical tools. AI systems increasingly operate across multiple layers of healthcare delivery. Some applications assist clinicians by analyzing medical data or suggesting treatment options. Others function within hospitals’ and health systems’ operational infrastructure, helping manage patient flow, prioritize diagnostic reviews, and allocate scarce resources.

These operational algorithms rarely capture headlines; however,  they shape the environment in which health care is delivered. Decisions about which cases are reviewed first, how clinicians allocate their attention, and how health systems manage capacity can profoundly influence patient outcomes.

For leaders responsible for health systems, artificial intelligence cannot be treated as simply another technological upgrade. It must be evaluated through governance structures capable of understanding how algorithms function, what assumptions shape their recommendations, and how their use aligns with institutional priorities.

Without that oversight, innovation risks amplifying complexity rather than improving care. Instead of informing, it can spread misinformation.

Aligning Artificial Intelligence With the Values of Medicine

Governance provides the policy foundation for responsible adoption of artificial intelligence, but real-world implementation reveals a second challenge: ensuring that AI systems operate effectively within healthcare delivery itself.

Large population health systems increasingly use advanced analytics to anticipate risk, manage chronic disease, and allocate clinical resources across diverse communities. Within these environments, artificial intelligence is no longer a theoretical innovation. It is already influencing how health organizations prioritize patients, coordinate care and deploy limited resources.

That operational perspective is central to Ran Balicer, MD, PhD, of Clalit Health Services, one of the world’s most advanced data-driven health systems. The Clalit integrated infrastructure connects hospitals, clinics, and community health programs through longitudinal datasets that support predictive analytics at the national scale.

Experience within such systems reinforces an important insight: artificial intelligence models do not function independently of human judgment. They reflect priorities embedded in their design and the assumptions guiding their deployment.

“Algorithms are opinions embedded in code,” Balicer has observed in discussions about the role of artificial intelligence in population health.

In practice, this means that AI systems interpret clinical data through frameworks shaped by human choices. The way a model defines risk, prioritizes cases, or recommends interventions reflects decisions about what matters most within a healthcare environment.

Those decisions carry ethical implications. When artificial intelligence helps determine which patients receive immediate attention or which cases are escalated for further review, transparency about how algorithms function becomes essential to maintaining trust among clinicians and patients alike. The scientific frontier of health-care AI reinforces that concern.

Isaac Kohane, MD, PhD, who has also served as a co-author of the Institute of Medicine Report on Precision Medicine, which has served as the template for national efforts, has spent decades exploring how machine learning can advance medicine while preserving the judgment that defines clinical practice. His research emphasizes that artificial intelligence in healthcare must align with the ethical traditions and professional responsibilities of medicine.

“AI systems in medicine must ultimately reflect the values of the profession they serve,” Kohane has written in discussions about AI alignment in biomedical informatics.

This perspective highlights a crucial distinction between technological capability and clinical responsibility. Many AI models entering healthcare environments were originally designed for broader computational tasks rather than the nuanced realities of patient care. Medicine operates within a landscape shaped by uncertainty, empathy, and accountability, and technologies introduced into that environment must reflect those values.

Ensuring that artificial intelligence aligns with the principles guiding health-care delivery, therefore, represents one of the most important scientific and ethical challenges facing the future of health.

The Discipline Required to Make Innovation Matter

The health sector has experienced waves of technological enthusiasm before. Electronic health records promised seamless information exchange, but then introduced administrative burdens on health professionals when implemented without thoughtful workflow design. Data analytics promised unprecedented insight, but sometimes led to fragmentation when systems failed to communicate across institutions.

Artificial intelligence now stands at a similar moment in the evolution of health technology.

Its capabilities in supporting decision-making flow are extraordinary, yet realizing them will require disciplined leadership to evaluate, integrate and govern AI tools within health-care delivery systems. Health leaders must learn to ask deeper questions before embracing the next algorithmic breakthrough. What problem does this system truly solve? How does it strengthen clinical practice? What assumptions guide its recommendations? How does its use advance the mission of improving patient health?

These questions move the conversation beyond technological novelty toward operational practicality. It’s among the many reasons these three global leaders step to the HIMSS stage together.

Artificial intelligence will undoubtedly reshape the health ecosystem in the years ahead. Its long-term impact, however, will not be determined solely by the sophistication of algorithms or the speed of technological progress. Along with how to leverage AI, ChatGPT and LLMs, users require heightened cognitive awareness.

It will be determined by whether the health community develops the discipline and ability required to translate innovation into systems that strengthen care, support clinicians and improve the health of the populations they serve.

The real story of artificial intelligence in health is no longer about what machines can do. It is about how wisely the health sector chooses to use them.

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I believe in making it very difficult for people to refuse vaccines. There’s enough of the libertarian about me that I wouldn’t actually strap them down and inject them, but I’m fine with school districts making parents write out their conscientious objections to children being immunised or with sports clubs requiring adult proof of immunisation before people can join. What I or you think is, though, beside the point. Much of the US is walking away from cajoling and compulsion and there’s great pressure in Europe for similar change. We can either go on moaning about how we wish the world hadn’t changed or we can respond effectively.

Before the current US Administration began rewriting vaccine recommendations, one in six US parents wasn’t following them. We used to joke that vaccine-preventable diseases in the West had become diseases of children of the over-educated middle classes who shopped at Whole Foods and did naked yoga classes; vaccine refusers now are still more likely to be white, but they skew to being conservative, very religious, and young. Recommendations actually reduced uptake in this group because most have a deep distrust of the Federal Government and its agencies.

Formal vaccine refusals in Poland more than doubled from 2017 to 2022 and reached over 87,000 in 2023, a 1685% increase since 2003; measles cases surged 10x in early 2024 due to falling rates. Ireland, where I live, has the third-lowest childhood vaccine coverage rate in the OECD.

There are bright spots too, Italy for example, and the battle is far from lost. But the mistrust now endemic to the United States is coming to Europe.

High-handed US and European experts

You can understand confusion, if not mistrust. About half of parents in the USA did not vaccinate their children for flu in the past year, compared with 41 percent who said they had done so, a Washington Post / Kaiser Family Fund poll found. Coverage started declining after 2019. In 2016, the US CDC said that the nasal flu vaccine used in children provided “no measurable benefit” (injectable vaccines for adults were, as usual, highly effective). In the same year, Public Health England said that the same vaccine (produced by a British company in a British factory) was 58 percent effective. Canada followed the UK, saying that its population was very different to the USA! It’s very unlikely that both the Americans and the Canadians were right — despite those obvious population differences…. Few journalists covered the story — I suspect because no-one wanted to be accused of promoting vaccine scepticism. The vaccine is now recommended again in the USA.

Few American paediatricians and even fewer nurses would have been able to explain this to parents because no-one ever bothered to give the professionals an explanation. What do we think doctors told parents who asked why a vaccination was recommended then was not and then was again? British parents who did a web search (this was pre-Chat GPT, remember) might have asked why their children were getting an apparently ineffective vaccine and would have met equally bemused stares from their health providers. Did anyone brief social media influencers or health journalists? Of course not, who do they think they are? What impudence…

I know some of those involved and I’m sure that there was no subterfuge and nothing sinister going on; the answer is likely to be dull and involve methodology and surveillance systems.

This is the way we all used to approach treatment discussions 40 years ago — the doctor told you what to do, you thanked him (it was nearly always a him) and you did it. Questions were a sign of disrespect, of even psychological illness. I was recently treated by a Russian dentist, now practising in Ireland, who was shocked and outraged when I questioned his recommendation to use antibiotics prophylactically; if he had been Irish, he would have been completely used to it.

Nonsensical recommendations in developing countries

Vaccine hesitancy looks a bit different in France. Those least likely to have their children vaccinated tend to be more educated, high users of the internet for information and to have lower trust in health authorities. Those who refuse vaccines for themselves tend to be at the lower end of the social hierarchy with less education and fewer financial resources. Many are ​immigrants and descendants of immigrants, and residents of French overseas departments. Both are probably likely to know about the vaccines which Western experts recommend for children in the developing world, including in Francophone countries.

I remember doing a policy interview with the health minister of a large Indian state. I was trying to find out what he might pay for an effective TB vaccine. “But”, he said, “we already have a TB vaccine. Why do I need a new one?” His top civil servant was sitting behind him and frantically gesticulating to me to try to stop me explaining that the BCG vaccine, given to almost every Indian newborn, may do nothing to prevent TB infections and, at best, may make the disease less severe in some of the children who contract it. It is, though, very good at causing severe side effects. No developed economy uses it; almost every poor one does.

I’m ashamed to say that I did not explain BCG as clearly as I should have to the minister. He was the norm, not the exception, in that series of policymaker interviews: few of those making decisions about TB vaccine policy had ever been given a thorough, honest briefing about the limitations of the vaccines their expert advisers recommended. None of the parents, of course, were ever told about any of these reservations.

There might also be a case for the current practice of giving many children in Africa and Asia a vaccine that sometimes causes polio, instead of preventing it, although I doubt it. The risks of a child contracting polio from the live-attenuated oral vaccine are probably underestimated when they’re presented to politicians and policy influencers. Hardly any parents who bring their children forward for these vaccines are told about the risk or the rationale for continuing to use them, rather than the perfectly safe inactivated vaccine used throughout the rich world.

Is it any wonder that those with insight into the developing world are sceptical? The real wonder is that vaccine confidence is still so high in Africa and Asia. That probably comes from everyday encounters with the tragic consequences of infection by vaccine-preventable illnesses, an experience blessedly denied to most Americans and Europeans.

What we need to do now

The road ahead has been cleared for us. Thirty years ago, I went out with a trainee doctor at the Royal College of Surgeons in Ireland. He was upset one evening because he had been berated by his tutor for telling an older patient that she had cancer — it had been agreed with the family that she would be told that she had a “growth” to avoid upsetting her. At least she found out: King George VI of the United Kingdom sent his daughter, Princess Elizabeth, on a world tour in 1952 because neither he nor she had been told that he had lung cancer and that it was terminal. He never saw her again. These stories shock us now because honesty, realism and communication are taken for granted in what we tell patients who are ill. These principles need to be the new basis for what we tell people who are healthy and want to stay that way.

First we need a change in attitude. Whether to be immunised or not is a decision that people will take — actually, a series of decisions. We don’t need to think about whether we like the concept or not, it is the way things increasingly are. We have to get ordinary people used to making good decisions, just as they do about other life issues such as house buying or insurance or continuing education. Ordinary people are not property experts or risk analysts or trained evaluators of course offerings, but they mostly make reasonable choices. They can do the same thing with vaccines.

Then, we need to communicate much more. Vaccine producers are free to talk to the public about recommended vaccines in many countries; where they are not, they need to be allowed to. Then they need to accept their responsibility to speak often, clearly and loudly. They are the experts on the vaccines they produce and they must tell potential recipients or the parents of recipients about the benefits and disadvantages. Of course, they need to do it in an honest and balanced way. They will be more successful if they communicate in partnership with professional organisations, charities and respected consumer groups. They can be transparent: they have a commercial interest in getting people to accept vaccines but a legal responsibility to set out all the factors in deciding whether to or not. It’s like banks selling mortgages and car dealerships selling warranties.

Researchers and healthcare providers need training in communication and answering questions. They need to be much better at helping policy makers to make decisions about vaccines. Today, too few vaccines are reimbursed and many are offered only to some of those who would benefit from them. In many countries, it is still too hard to get vaccinated and even where rules have changed, practices have not — look at Poland, for example. Politicians and public officials can unleash vaccines so that they can do even more to boost productivity, growth and wealth in society.

Those same scientific and medical experts need to be much better at talking to people who are making decisions about immunisation. Research tells us clearly what helps the right decision, but too few professionals follow the evidence. The most powerful prompt to action is a trusted health professional saying, “I would like you to do this”. Setting a good example works wonders too, but too few health professionals have had all of the vaccines recommended for them.Communication can change all of this.

The vast majority of social media influencers want to give good advice and powerful motivation but no-one talks to them — after all, we want people to follow the guidelines, not think, don’t we? For example, have you seen Dr Mike Varshavski take on 20 vaccine sceptics at once? Thirty million people probably have over various platforms and he’s brilliant. Industry and professionals need to work with influencers who specialise in women’s issues, childhood, workplace effectiveness and, of course, health. Look at this from Dr Ahmed Ezzat — his videos on RSV reduced calls to the emergency services by 25% — and just think what he can do for vaccines.

Journalists are discouraged from writing pieces about vaccine decisions — “just tell people to follow expert recommendations”. Many, consequently, avoid writing about vaccines. We need to treat these journalists as powerful allies in helping lay people to make important decisions with lifelong implications for their risk of developing chronic illnesses. It’s the way that property developers treat journalists who write about houses,

Honestly, I still think it would be simpler and still ethically correct to just nudge almost everyone into getting immunised but that is not an option in many places now and, given the global market in ideas, won’t be one anywhere soon.

Parents get things right

Asia should encourage us. Many parents save and spend to get their children the best vaccines. The state often provides old tech or nothing, so middle-class parents take their children to private clinics for the best protection and pay full price for it. Of course, it’s not fair to poorer children and it is crazy public policy given that population sizes will plunge across Asia over the next 30 years so every child, whether middle class or not, is a precious national resource. Still, it shows that individual families can and do make better decisions than health policy makers when the routes of communication are open and used well.

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Scientific Promise and Practical Benefits

A major strength of brain organoid research is its potential to improve our understanding of neurological and psychiatric conditions. Researchers can generate organoids from people with known genetic mutations to study how specific genes affect early brain development. This method has been used to study conditions like autism spectrum disorders, epilepsy, schizophrenia, and Alzheimer’s disease. It helps scientists find cell abnormalities that might not show up in animal studies.

Brain organoids are also useful for drug discovery and safety testing. Many treatments that work in animal models do not succeed in humans, especially for brain disorders. Organoids give scientists a human-based way to test how drugs affect neural cells. This can help spot toxic effects or benefits earlier, potentially lowering the risk of expensive late-stage failures and reducing unnecessary testing on people.

Limitations, Misconceptions, and Ethical Concerns

Even though brain organoids show promise, they have important limitations that are sometimes missed in public discussions. They lack blood vessels, immune cells, and sensory input, all of which are needed for normal brain function. Because they lack a vascular system, organoids obtain oxygen and nutrients only by diffusion, which limits how large and mature they can become. Most organoids end up looking like early fetal brain tissue, not fully developed brains. Does the appearance of something mean it will have the same abilities?

Variability is another challenge. Organoids grown in different laboratories — or even within the same lab — can vary in structure and cellular composition. This makes standardization difficult and complicates the interpretation of results. Additionally, reports of electrical activity within organoids have sometimes been mischaracterized as evidence of consciousness. Most neuroscientists agree that current organoids do not possess awareness, sensation, or thought, but the debate highlights broader uncertainties about how consciousness arises in biological systems.

As the science has advanced, ethical questions have also increased. There are concerns about informed consent when donor cells are used to make neural tissue, especially if donors did not know this could happen. Other worries come up when human organoids are put into animals, which raises questions about species boundaries and oversight. Although these experiments are closely regulated, many ethicists say clearer rules are needed as the technology develops.

Future Directions and Responsible Progress

Researchers are now trying to make brain organoids more realistic and useful. They are working on adding vascular-like systems, combining different organoid types to study how brain regions interact, and making results more consistent between labs. These improvements could help us better understand complex brain disorders and lead to more personalized treatments.

At the same time, ethical guidelines are changing to keep up with new scientific advances. Many experts say that as organoid research moves forward, it should be matched by openness, oversight from different fields, and regular public involvement. Brain organoids are not miracle cures or major threats; they are powerful but imperfect tools that can help neuroscience when used carefully. The future of this research will depend on both technical progress and a strong focus on ethics and public trust.

If all of this sounds like something from a Frankenstein movie, that would be one approach to take, but it isn’t realistic. We are only at the very beginning of understanding what the potential and the problems involved are for us. The research holds great promise, but it also requires informed restrictions that will not prevent advances.

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