Milan Toma

The Psychology of Health

Health EN ↓ 17 episodes

Each episode is a clear, accessible synthesis of research studies on timely and controversial health topics; no hot takes, no hype, just what actual science says. Hosted by Milan Toma, Ph. D., this podcast cuts through the noise. Instead of speculation and hearsay, you’ll get evidence-based insights on everything from sleep and weight gain to the anatomy of misinformation and the psychology behind public health debates. If you’re frustrated by the flood of opinions online and want to know what the research really shows, this is the show for you.

Author

Milan Toma

Category

Health

Podcast website

www.dawningresearch.org

Latest episode

May 23, 2026

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Episodes

AI Can't Replace Your Doctor: Why the Headlines Are Lying to You 23.05.2026

Those viral headlines claiming AI diagnoses better than doctors? They're based on fundamentally flawed research—and believing them could be dangerous to your health. In this episode, I break down the alarming trend of studies from prestigious journals like  Science  and  JAMA Network Open  claiming large language models outperform physicians in diagnostic reasoning. The problem? These studies...

The Limits of Chatbots in Clinical Decision‑Making 07.05.2026

Chatbots and large language models are becoming increasingly common in everyday life, but their growing presence in healthcare has raised an important question:  Should probabilistic AI systems be used to help make medical decisions?  This episode takes a clear, grounded look at why the answer is far more complicated—and potentially far more dangerous—than many people realize. Modern chatbots work...

Viral AI-Beats-Doctors Study 04.05.2026

Another week, another headline declaring AI has officially surpassed physicians. This time, it's a study published in  Science  on April 30, 2026, claiming that OpenAI's o1 model "outperformed physician baselines" across multiple diagnostic reasoning tasks. The research comes from Harvard, Stanford, and Beth Israel Deaconess Medical Center. It's rigorous. It's peer-review...

Medical Education Must Teach AI Differently 14.04.2026

Artificial intelligence is rapidly moving into classrooms, clinics, and daily healthcare decision making, but much of the public conversation is built on a dangerous misunderstanding. Too often, people now treat artificial intelligence as if it simply means chatbots. In this episode, Dr. Milan Toma explains why that confusion matters and why healthcare professionals must learn to distinguish betwe...

The Overfitting Trap 02.04.2026

Introduction: A Tale of Two Rounds Every attending physician has seen the "Star Student" who can quote the New England Journal of Medicine verbatim but freezes when a patient doesn't follow the script. In this episode, we introduce Student A and Student B . Student A (The Memorizer): They have a mental database of every practice vignette. They are fast, confident, and statistically &...

Understanding the Trust Gap in Medical AI 18.03.2026

Have you ever wondered why skepticism about artificial intelligence persists in healthcare, even as new AI tools are rapidly introduced? In this episode, Dr. Milan Toma, Associate Professor of Clinical Sciences at NYIT College of Osteopathic Medicine, explains the roots of distrust in clinical AI systems and what it takes to regain confidence. Drawing on decades of machine learning evolution, real...

Algorithmic Shortcuts That Undermine Medical AI 13.03.2026

Imagine you are developing an AI system to predict which patients are at risk of becoming obese based on their lifestyle factors. You gather data on diet, exercise habits, sleep patterns, stress levels, and dozens of other variables. You train your model. It achieves 99% accuracy. You celebrate. Then someone points out that you included the patients' current weight in your dataset. Your model...

The Accuracy Trap 09.03.2026

When a ninety nine percent accurate AI misses every single case of disease, something has gone terribly wrong. In this episode, Dr. Milan Toma exposes one of the most dangerous pitfalls in medical artificial intelligence: the accuracy paradox. Discover why impressive accuracy numbers can mask complete clinical failure, and why that four percent drop in accuracy might actually save lives. Dr. Toma...

A Clinical Guide to AI in Medical Diagnostics 20.11.2025

What can a 2017 colonoscopy study teach us about using AI diagnostics safely in 2025? An AI diagnostic tool boasts 99% accuracy. Should you trust it? In this episode, I explain why that number can be dangerously misleading and equip medical professionals with the practical strategies needed to see through the hype and protect their patients. As artificial intelligence becomes more integrated into...

AI in Today's Business Landscape 16.11.2025

Are we caught in another tech hype cycle with AI? The promise of technology often clashes with financial and practical reality. This presentation cuts through the noise to offer a clear and balanced framework for thinking about the future of artificial intelligence in business and society. Join me for a thoughtful exploration of how to navigate the booms and busts of innovation. Using the "Co...

How We Burn Energy 28.10.2025

This episode provides an overview of  Total Energy Expenditure (TEE)  and  Basal Energy Expenditure (BEE) , emphasizing their definitions, measurement methods, and patterns across the human lifespan. It explains that  TEE  is the total of all calories burned, comprised mainly of  BEE  (the energy needed for basic functions at rest), the  Thermic Effect of Food , and  Physical Activity Energy Expen...

Yo-Yo Dieting 27.10.2025

This episode challenges the common belief that weight gain in middle age is caused by a  slowing metabolism , asserting that adult metabolism generally remains stable until after age sixty. Instead, it explains that weight gain is primarily driven by  lifestyle factors , such as reduced physical activity and increased caloric intake. It details how the body mounts a  powerful biological defense  a...

Midlife Metabolism: Fact vs. Fiction 26.10.2025

This episode fundamentally challenges the popular belief that human metabolism slows down during midlife, asserting instead that  total energy expenditure remains stable between the ages of 20 and 60 , only declining after age 60. Using gold-standard measurements, the text explains that the weight gain many adults experience is not due to a slowing metabolism but rather to  changes in body composi...

Underreporting Bias in Obesity Research 25.10.2025

This episode provides an overview of the significant challenge of  dietary underreporting bias  in nutrition research, especially concerning individuals with obesity. It explains how some people with higher body mass index (BMI) systematically and substantially under-report their true food and calorie consumption, often by 700 to 850 kcal per day, which is far more than their lean counterparts. Th...

Dietary Trends, Reporting Bias, and the Obesity Epidemic 24.10.2025

This episode provides an extensive critique of the reliability of  self-reported dietary surveillance data , arguing that simple correlations between dietary trends (like increased calories or changes in macronutrients) and the rise in  U.S. obesity rates  are misleading. The episode emphasizes that  correlation does not equal causation  and highlights the significant problem of  systematic underr...

The Biology of Appetite Regulation 23.10.2025

This episode provides an extensive overview of the  complex biological regulation of appetite  and energy balance, moving beyond the simple "calories in, calories out" model. It establishes that  fat tissue is an active endocrine organ  that produces hormones crucial for signaling the brain about the body's energy status. The episode highlights the central role of the hormone  leptin...

Sleep's Influence on Weight Gain 22.10.2025

This episode provides an extensive overview of the strong relationship between  insufficient sleep  and the increased risk of  weight gain  and  metabolic dysfunction . It emphasizes that epidemiological data consistently identify short sleep duration as an independent risk factor for obesity, particularly in younger populations. Mechanistically, sleep deprivation is shown to disrupt  appetite-reg...

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