Latest / Morning Medical with Dr. Chow / AI in Healthcare, Alzheimer's Breakthrough, and Mental Health Impacts

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AI in Healthcare, Alzheimer's Breakthrough, and Mental Health Impacts · Morning Medical with Dr. Chow

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Welcome to Morning Medical with Dr.
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Chow, where we bring you the latest in medical and health news from around the world using the
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power of artificial intelligence.
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I'm your host, Dr.
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Chi-Ming Chow.
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Today, we're diving into the fascinating world of digital health and artificial intelligence,
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exploring how these technologies are shaping the future of healthcare.
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In a recent discussion, Caroline Pearson, the executive director of the Peterson Health
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Technology Institute, shed light on the promises and challenges of digital health
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companies.
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These companies often pitch to investors with a straightforward proposition: support us, and
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we'll deliver better patient care at a lower cost.
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It's a compelling narrative, but Pearson cautions that there's still a lack of concrete
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evidence showing that some of these industry leaders are making a significant impact on
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patient health outcomes.
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Artificial intelligence is heralded as a transformative force in healthcare, promising
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to revolutionize how we diagnose, treat, and manage diseases.
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However, while the potential is immense, the journey from promise to practice is fraught
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with challenges.
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The digital health sector is a vibrant ecosystem of innovation, yet it faces scrutiny
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over whether its solutions truly translate into better care.
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Pearson's insights remind us that while the technology is advancing rapidly, the evidence
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base supporting its efficacy is still catching up.
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This gap highlights the need for rigorous evaluation and validation of digital health
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tools.
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It's crucial that these innovations are not only groundbreaking but also genuinely
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beneficial to patients.
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As we continue to explore the digital health landscape, it's important to keep a critical
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eye on the evidence supporting these new technologies.
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Balancing innovation with proven outcomes will be key to ensuring that digital health fulfills
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its promise of enhancing patient care while reducing costs.
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Let's delve into an intriguing shift in healthcare communication—plain language as a
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competitive advantage.
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For years, healthcare and medical device marketing prioritized scientific precision over
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simplicity.
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This trade-off is no longer viable.
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As artificial intelligence-driven health experiences reshape how people consume and
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interpret information, clarity has become the baseline expectation rather than a secondary
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concern.
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The companies that will gain an edge over competitors in the next decade will not be the
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ones that explain more, but those that explain better.
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The introduction of ChatGPT Health has set new standards for what is considered
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'understandable.' Consumers now expect health information to be interpreted, contextualized,
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and actionable.
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Plain language, available on demand, is explaining lab results, symptoms, coverage
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decisions, and care pathways.
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These experiences are not only clear but also come with explicit trust controls.
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For instance, OpenAI's ChatGPT Health does not use chats, files, and memories to train its
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foundation models, reinforcing transparency and consumer confidence.
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The signal for health technology leaders is not just the product itself but what it represents.
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When targeted audiences can instantly receive clear explanations of complex health
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information, dense, acronym-heavy messaging no longer reads as 'professional.' Instead, it
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reads as friction.
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At that point, brands are no longer competing solely with peers; they are competing with
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clarity itself.
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Our recent research analyzed 5,472 unique citations generated by multiple major chatbots
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in response to health-related prompts.
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We found that 59 percent emphasized consumer-friendly summaries or scientific
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interpretation rather than raw data.
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This pattern helps explain why translation and synthesis now dominate health discovery.
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In moments of uncertainty or stress, users gravitate toward sources that help them
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understand what this means and what to do next.
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For marketing leaders, this represents a significant shift in competitive advantage.
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The market is moving from content volume to content comprehension.
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Organizations that reduce uncertainty quickly gain the most attention, trust, and
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conversions.
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Plain language is framed as a health literacy issue, but medical device and health technology
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executives should treat it as a growth and efficiency lever.
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Conversions improve because decision friction drops, and cost-to-serve declines because
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confusion ceases to generate work.
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Trust strengthens because clarity signals integrity.
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In regulated markets, vague phrasing is often a defensive strategy, but it can ironically erode
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trust.
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Clear, direct explanations paired with clinical rigor signal transparency and credibility.
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Risk decreases because misunderstanding decreases.
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Misunderstood instructions can cause clinical harm, and misread disclaimers can produce false
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confidence.
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Plain language lowers the probability of misinterpretation while preserving accuracy.
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Organizations do not realize sustained gains from clarity by editing copy ad hoc.
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Operationalizing plain language as infrastructure is a competitive advantage.
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It starts with identifying high-friction moments like onboarding, product limitations,
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and reimbursement explanations.
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Effective teams govern clarity by defining reading-level targets by content type,
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maintaining approved glossaries, and standardizing structural formats to ensure
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information is easy to scan and act on.
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Finally, clarity must be measured.
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Reduced follow-up contacts, higher completion rates, shorter sales cycles, and lower
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abandonment rates tie plain language directly to outcomes that executives already prioritize.
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Treating plain language as a cross-functional growth initiative gives organizations a
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competitive edge.
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The best place to start is wherever your funnel is already leaking.
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Look at signals showing where prospects are getting stuck, such as sales call notes and CRM
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activity.
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From there, alignment matters.
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The explanation on the website should match what appears in sales decks, customer portals,
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and follow-up communications.
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Consistent language builds confidence and accelerates action.
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The future standard is information that is clear, contextual, and trustworthy by design.
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Organizations that respond effectively will make their materials effortless to understand,
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treating plain language as core infrastructure for growth, trust, and market relevance.
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Today's discussion brings us a fascinating breakthrough in Alzheimer's prevention,
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stemming from a decades-old seizure medication.
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Researchers have discovered that the anti-seizure drug levetiracetam, which was
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first approved by the United States Food and Drug Administration in 1999 under the brand
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name Keppra, shows potential in preventing Alzheimer's disease.
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This medication has been primarily used to treat partial-onset seizures, but recent
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findings suggest it could also play a role in Alzheimer's prevention.
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The study, conducted by researchers at Northwestern University, found that
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levetiracetam prevents the formation of toxic amyloid beta peptides.
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These peptides are small protein fragments commonly seen in the brains of Alzheimer's
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patients and are believed to be a significant factor in the development of the disease.
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The medication was shown to prevent the formation of amyloid-beta 42 in both animal
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models and cultured human neurons, which are critical in understanding the disease's
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progression.
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Interestingly, the effect was also observed in post-mortem human brain tissue from individuals
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with Down syndrome, a group at high risk for Alzheimer's.
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This observation adds another layer to our understanding of how this medication could
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potentially be used in diverse populations at risk for Alzheimer's.
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The study's lead author, Jeffrey Savas, emphasized that while current Alzheimer's drugs
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focus on clearing existing amyloid plaques, levetiracetam works by preventing the
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production of amyloid-beta 42 peptides and plaques.
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This discovery not only uncovers new biological insights but also opens doors for developing
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new drug targets.
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The researchers highlighted that the brain naturally avoids producing toxic amyloid-beta
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42 proteins in younger years, but this ability diminishes with age.
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In individuals developing Alzheimer's, an increase in these proteins leads to the
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formation of tau tangles—abnormal protein clumps that can kill brain cells and trigger
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neuroinflammation, ultimately resulting in dementia.
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To be effective as an Alzheimer's prevention method, levetiracetam would need to be
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administered to high-risk patients very early, potentially up to twenty years before elevated
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amyloid-beta 42 levels are detectable.
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This early intervention could be crucial in delaying or preventing the onset of Alzheimer's
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symptoms.
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The research team conducted a retrospective analysis of previous human clinical data and
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found that Alzheimer's patients taking levetiracetam experienced a significant delay
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in cognitive decline compared to those who were not on the drug.
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Although the change was modest, spanning a few years, it supports the drug's potential to slow
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Alzheimer's progression.
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However, the study does have limitations.
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It primarily relied on animal models and cultured cells, without conducting human
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trials.
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Therefore, while the findings are promising, further research is necessary to confirm these
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results in human subjects.
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The researchers are now working on developing an improved version of levetiracetam that would
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remain in the body longer and better target the mechanisms preventing plaque production.
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This enhancement could make the drug more effective in preventing Alzheimer's disease.
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As with any medication, there are side effects to consider.
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Common side effects of levetiracetam include drowsiness, dizziness, and irritability, with
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more severe effects like mood changes and allergic reactions occurring less frequently.
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These potential risks underscore the importance of careful consideration and monitoring in its
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use for Alzheimer's prevention.
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The United States Food and Drug Administration has issued warning letters to thirty telehealth
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companies over misleading claims regarding compounded glucagon-like peptide-1 receptor
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agonist products.
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These letters specifically address claims that these compounded products are the same as those
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approved by the Food and Drug Administration.
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Additionally, the violations include companies branding these medications with their own names
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or trademarks, which falsely implies that they are the manufacturers or the compounders of
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these drugs.
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This action is part of a broader crackdown on misleading direct-to-consumer pharmaceutical
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advertisements that began last September.
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Since then, the Food and Drug Administration has issued thousands of warning letters,
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surpassing the total number sent over the previous decade.
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According to Food and Drug Administration Commissioner Marty Makary, this marks "a new
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era of enforcement" where the agency is closely monitoring claims made by telehealth and
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pharmaceutical companies across all media platforms and is ready to take swift action.
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Compounded drugs can indeed play a critical role, especially in overcoming drug shortages
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or meeting unique patient needs.
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However, the Food and Drug Administration stresses that compounders should not attempt to
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bypass the agency's approval process by mass-marketing these compounded drugs as
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equivalent to approved products.
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Recently, the Food and Drug Administration announced plans to take decisive steps against
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non-Food and Drug Administration-approved glucagon-like peptide-1 receptor agonists.
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This regulatory shift has already led to telehealth provider Hims & Hers withdrawing its
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newly launched compounded semaglutide, branded as Wegovy, from the market.
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The decision followed significant regulatory and legal pressure, just days after the
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product's debut.
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The recent privatization of online gambling in Ontario has led to a dramatic increase in the
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number of young men reaching out to the province's mental health helpline for
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gambling-related issues.
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According to a study published in the Canadian Medical Association Journal, contacts to the
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helpline from males aged fifteen to twenty-four have surged by 317 percent since the
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introduction of private online gambling.
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This significant rise points to a need for stronger harm-reduction measures and increased
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access to treatment for gambling disorders.
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The study examined data from ConnexOntario, a free twenty-four-hour mental health and
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addictions helpline, spanning from January 2012 to September 2025.
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The researchers noted that the number of gambling-related contacts increased notably
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after the launch of the government-operated gambling platform PlayOLG in January 2015, and
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even more so following the expansion of private online gambling in April 2022.
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Over the thirteen-year period covered by the study, ConnexOntario received more than 745,700
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contacts, with approximately 37,000 of these being related to gambling.
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Among young males aged fifteen to twenty-four, the monthly rate of gambling-related outreach
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rose by 317 percent after the privatization of online gambling.
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Men aged twenty-five to forty-four also saw an increase of about 108 percent over the same
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period.
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Dr.
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Daniel Myran, a research chair at North York General Hospital and co-author of the study,
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suggests that increased awareness of the helpline might contribute to the rise in calls.
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However, he cautions that the surge is not solely due to existing problems but also
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potentially linked to the higher volume of gambling marketing since privatization.
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He emphasizes the need to consider who these advertisements are targeting and the messages
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they convey, advocating for restrictions on ads that are widely visible to youth.
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The study highlights concerns over micro-betting and other forms of gambling
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associated with higher addiction risks.
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Micro-betting allows individuals to place bets on events within a game, which can lead to
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"loss chasing," where gamblers try to recoup losses by placing additional bets.
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This behavior can accelerate the development of gambling disorders.
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It is crucial to treat gambling disorders as a public health issue, according to Dr.
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Myran.
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Individuals with gambling disorders face a high risk of mental health conditions, including
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self-harm and suicide.
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The impact of gambling can extend beyond the individual, affecting families and communities.
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The study underscores the broader health implications of gambling and the need for
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comprehensive treatment options.
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Dr.
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Luke Clark, director of the Centre for Gambling Research at the University of British Columbia,
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notes that the study's long timeframe, starting from the early days of online gambling, is a
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strength.
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He points out that while Ontario's situation is unique due to its privatization, the findings
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could have implications for other provinces where online gambling is government-operated.
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The study serves as a call to action for policymakers to consider the broader health
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implications of gambling and the potential societal impact.
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As gambling continues to grow, particularly among young people, there is an urgent need for
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effective regulation and public health strategies to mitigate the risks associated
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with gambling addiction.
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Chatbots powered by artificial intelligence are increasingly being used by people seeking
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health advice, but it's crucial to understand their limitations.
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In recent months, technology companies like OpenAI and Anthropic have released programs
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specifically designed to answer health-related questions.
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OpenAI's ChatGPT Health, for instance, is a new version of its chatbot that can analyze users'
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medical records, wellness apps, and wearable device data to provide insights into health and
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medical queries.
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However, it's important to note that these AI-driven tools are not substitutes for
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professional medical care and should not be used to diagnose medical conditions.
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These chatbots are designed to help users interpret complex test results, prepare for
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doctor visits, and analyze health trends found in medical records and app metrics.
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Unlike a generic Google search, these AI platforms can offer more personalized
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information by considering the user's medical history, such as prescriptions, age, and
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doctor's notes.
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This personalized approach is seen as an improvement over the current status quo, where
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individuals might otherwise have little guidance or rely on their own understanding.
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Robert Wachter, a medical technology expert at the University of California, San Francisco,
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highlights that while AI platforms are not infallible and can sometimes provide inaccurate
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advice, they offer a potentially useful resource when used responsibly.
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The information generated by these tools is often more tailored and specific than what
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patients might find through general online searches.
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Wachter emphasizes the importance of providing chatbots with as much detail as possible to
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improve the quality of responses.
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It's essential to recognize that AI chatbots are not suitable for medical emergencies.
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Symptoms such as shortness of breath, chest pain, or severe headaches could indicate
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serious conditions requiring immediate medical attention.
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Lloyd Minor, the dean of Stanford University's medical school, advises that for significant
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medical decisions, people should not rely solely on AI tools.
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A healthy degree of skepticism is necessary, and professional medical advice should be
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sought for major health concerns.
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There are also privacy concerns associated with using AI chatbots for health advice.
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The Health Insurance Portability and Accountability Act of 1996, which protects
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sensitive medical information, does not apply to companies that design chatbots.
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This means that users' personal health information shared with these companies might
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not be as secure as it would be with healthcare providers.
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Both OpenAI and Anthropic assert that they keep users' health data separate from other data
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types and offer additional privacy protections.
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Users must opt-in to share their information and have the option to disconnect at any time.
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Despite the excitement surrounding AI, testing of these technologies is still in the early
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stages.
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A study conducted by Oxford University found that while AI chatbots could correctly identify
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medical conditions 95 percent of the time when presented with comprehensive written scenarios,
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they often faltered during real interactions with users.
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Participants frequently did not provide the necessary information for the chatbots to make
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accurate assessments, and the bots sometimes offered a mix of accurate and inaccurate
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information that users struggled to differentiate.
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The ability for chatbots to ask follow-up questions and obtain key details is an area
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with potential for improvement.
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Robert Wachter suggests that AI tools will become more effective when they can engage in
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dialogue similar to interactions with doctors, asking pertinent questions and providing
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feedback.
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Seeking a second opinion by consulting multiple chatbots, such as ChatGPT and Google's AI tool
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Gemini, might offer users more confidence in the information received.
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As we wrap up today's episode, let's explore how artificial intelligence is reshaping the
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workforce in healthcare, a topic that was recently highlighted in an article by Sylvana
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Quader Sinha in Forbes.
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The divide emerging in healthcare is no longer about whether organizations use artificial
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intelligence but rather how they integrate it into their workforce strategy.
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Artificial intelligence is becoming essential infrastructure, driving a significant shift in
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how healthcare is delivered.
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The healthcare sector is facing a critical constraint, and it's not technological—it's
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human.
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The demand for care is outpacing what the current clinical workforce can sustainably
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provide, especially with aging populations and the ongoing issue of burnout.
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As a result, there's a widening capacity gap that hiring alone cannot address.
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In this environment, artificial intelligence has moved from being an innovative experiment
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to an essential part of restructuring healthcare delivery.
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At a recent panel in Davos, leaders from various sectors discussed the role of
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artificial intelligence in healthcare.
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Hervé Balzano, President of Health at Mercer, emphasized that artificial intelligence is
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already a part of healthcare.
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The pressing question now is how deliberately it will be integrated into the architecture of
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care.
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Dr.
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Maria Ansari, CEO of The Permanente Medical Group, highlighted that artificial intelligence
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was not deployed as a showcase initiative but to address real, measurable strain.
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By using artificial intelligence tools like ambient scribes and automated patient
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communication, her organization saw significant improvements in physician retention and patient
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satisfaction.
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The economic logic behind integrating artificial intelligence into healthcare systems
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is compelling.
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In value-based systems, the focus is on doing better, not more.
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Artificial intelligence tools were retrained to support self-management and reduce unnecessary
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visits, moving care upstream and reducing downstream costs.
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This approach turns wellness into an asset and positions artificial intelligence as a key
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component of workforce retention strategies.
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Beyond productivity, artificial intelligence is transforming diagnostics.
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Amira Romani from Siemens Healthineers described how artificial intelligence-enabled
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imaging reduced MRI scan times significantly, allowing radiologists to care for more
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patients.
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This not only eliminates diagnostic backlogs but also stretches specialist capacity further,
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making expertise more scalable.
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Philips' collaboration with the Gates Foundation demonstrated how artificial
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intelligence-guided handheld ultrasound allows minimally trained workers to perform screenings
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in rural settings, thus extending the reach of scarce expertise.
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From an investment perspective, artificial intelligence is evolving into "systems of
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systems," where multiple artificial intelligence agents oversee and cross-check
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each other.
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This infrastructure layer has the potential to accelerate real-world evidence generation and
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administrative processes at scale.
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However, realizing this potential requires more than just technical feasibility; it demands
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organizational integration and redesigning roles, workflows, and incentives around
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artificial intelligence.
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Scaling artificial intelligence responsibly requires trust and governance.
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It's crucial to be clear about what artificial intelligence can and cannot do.
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Oversight is essential for safety and trust, but current regulatory systems were not
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designed for adaptive artificial intelligence architectures.
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Incentive design also plays a crucial role.
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In fee-for-service environments, reducing unnecessary visits can conflict with revenue
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models, whereas, in value-based systems, prevention and early intervention align more
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naturally with artificial intelligence-supported triage and risk
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identification.
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The emerging divide in healthcare is not about whether organizations use artificial
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intelligence but how they use it.
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Some systems pursue incremental efficiency gains, while others are redesigning
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documentation flows, diagnostic networks, and triage models to preserve and redistribute
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scarce clinical capacity.
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In a system where human clinical capacity is the scarcest resource, treating artificial
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intelligence as optional experimentation is not prudence but postponement.
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In conclusion, healthcare leaders now face a strategic choice: layer artificial intelligence
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onto existing strain or redesign their operating models around it.
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As demand accelerates beyond workforce supply, this choice will define which systems remain
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sustainable.
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Thank you for joining us on Morning Medical Dr.
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Chow.
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I hope today's episode has provided valuable insights into the evolving landscape of
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healthcare.
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Until next time, stay informed and stay healthy.