The Missing Links in Applying Lifestyle Medicine: Education, Infrastructure and AI

Chronic disease won’t wait. AI can help bridge the gap between medical knowledge and personalized care, but only if it understands the whole patient and not just generate faster answers.

After nearly 30 years practicing Family and Lifestyle Medicine, I've become convinced that better education, stronger infrastructure, and thoughtfully designed AI can help clinicians deliver the preventive care patients need to live longer and healthier lives.

Lifestyle Medicine is an evidence-based medical specialty that complements conventional care by preventing, treating, and sometimes reversing chronic disease through sustainable lifestyle changes such as nutrition, physical activity, and stress management. But it is more than that: Lifestyle Medicine is the foundation health is built on, and it should therefore be the cornerstone of our healthcare system. This specialty has long demonstrated that many chronic diseases can be significantly reduced and even reversed, but only when clinicians address the underlying causes of disease and translate the findings into improving everyday care.

The American College of Lifestyle Medicine supports that intensive lifestyle changes can reverse and prevent chronic conditions like type 2 diabetes, obesity and cardiovascular disease. One landmark diabetes study showed that type 2 diabetes can be put into remission through significant, sustained weight loss. It found that an intensive weight management program helped nearly half of participants achieve remission in 12 months.

I've seen this firsthand in my own practice, where patients with type 2 diabetes have reduced or even eliminated their need for medications, including insulin, through sustainable lifestyle changes. By addressing the underlying causes of insulin resistance rather than simply managing blood sugar levels with medication, Lifestyle Medicine can produce meaningful and lasting improvements in health.

These improved patient outcomes that I've witnessed throughout my career are increasingly supported by scientific evidence. But despite decades of research demonstrating its effectiveness, Lifestyle Medicine still isn't a standard part of care, and the cost of that gap is enormous.

According to the Centers for Disease Control and Prevention, chronic and mental health conditions account for roughly 90% of U.S. healthcare spending, while chronic disease is projected to cost the U.S. economy nearly $47 trillion over 15 years. Much of this burden is associated with a relatively small set of modifiable factors, including nutrition, physical activity, sleep, stress, and social connection.

The evidence on what drives chronic disease, and how to prevent much of it, is well established. Translating that knowledge into everyday practice is where we fall short, and one of the biggest barriers is education. Medical training has historically devoted too little attention to foundational disciplines that are essential to preventing and treating chronic disease, including clinical nutrition and menopause care.

Approximately three-quarters of U.S. medical schools still do not require a dedicated nutrition course. Nutrition and metabolic health affect nearly all chronic disease physicians treat, yet many physicians finish training barely equipped to use nutrition as a therapeutic tool. That is a gap medical education can no longer afford to leave open.

The educational gaps extend beyond nutrition. Many physicians also receive little formal training in menopause care, despite its significant impact on cardiovascular health, metabolic health, sleep, cognition, mental well-being, and overall quality of life. Fewer than 7% of obstetrics/gynecology and internal medicine residents report feeling adequately prepared to prescribe hormone therapy. That leaves millions of patients whose physicians were never trained to prevent or treat their conditions early.

As clinicians, we are trained to diagnose disease, interpret increasingly complex diagnostic tests, and manage multifaceted treatment plans. Yet we are also expected to help patients make lasting changes to behaviors such as nutrition, physical activity, sleep, and stress management. That is not a small task. Meaningful behavior change requires time, continuity, coaching, and a deep understanding of each patient's daily life. These conversations can rarely happen during a short, 15-minute office visit.

Consider a patient with type 2 diabetes whose blood sugar had significantly improved through diet and physical activity but now only sleeps a few hours each night because of caring for an aging parent. At the same time, she is also struggling with menopausal symptoms and financial stress that make healthy routines such as eating nutritious foods and exercising daily too difficult to sustain. These circumstances may influence her long-term health more than her latest A1C level.

Pieces of that story may exist in primary care notes, specialist visits, wearable devices, pharmacy records, nutrition counseling, behavioral health visits, and conversations that never make it into the medical record. Rarely does all of this information exist together in one place. Electronic health records document clinical encounters, but they were never designed to synthesize a patient's complete health context across providers, settings, and everyday life. As a result, clinicians end up assembling scattered pieces during a brief office visit, which makes truly personalized prevention extraordinarily hard.

Artificial intelligence can help bridge this gap. Not by replacing clinicians, but by synthesizing these disorganized sources of information, identifying meaningful patterns, and giving clinicians a more complete understanding of the whole patient. Used responsibly, AI can strengthen clinical judgment and help translate what we already know about prevention into more personalized and effective care.

As healthcare organizations pour money into more sophisticated AI capabilities, the question is moving past whether AI belongs in medicine to what it should do. Following closely sits a more urgent question of how do we know which AI health systems to trust.

In one recent industry survey, 73% of healthcare and life sciences executives reported positive returns on generative AI within the first year, and nearly half said they are now directing most of their AI investments toward systems that can reason and act rather than simply summarize. That shift raises the stakes. A tool that only summarizes can be wrong in ways that are easy to catch, but one that reasons and acts on a patient's behalf has to clear a far higher bar.

I believe any such AI system has to meet three tests before it comes near patient care: transparency, human clinical judgment, and patient-centered interoperability. Physicians should be able to understand how recommendations were reached, what evidence informed them, and where uncertainty exists. The system should organize complex information for physicians, not make the call for them, because medical decisions belong with the clinicians and care team. Since patients are treated across many providers and settings, their health information should follow them rather than stay locked to a vendor, so physicians have a more complete picture of the factors that influence their health. These are not features to evaluate after the fact. They are the standards we should demand before any AI system enters patient care.

For prevention to finally become the standard of care rather than the goal we keep deferring, we must invest in education, modernize the infrastructure, and hold AI to those standards as we put it to work in understanding the whole patient. Chronic disease is not waiting for us to get this right, and neither should we.