A pediatrician went on a podcast, then found her own expertise inside a stranger's AI. Sonal Patel, who runs a home health clinic for families in the first months after birth, built her own tool instead. She explains how it happened, why she thinks physicians are the last group who should be sitting this one out, and why the real risk is not the technology but the colleague who learns it first.

⏱️ Chapters:
0:00 Introduction
1:20 The stranger in Silicon Valley who used her expertise
2:24 Why an AI can never capture the art of medicine
3:08 The $880 her own AI saved families
3:51 The 80 percent of medicine that tech is building on
4:24 The three months after birth that med school skips
5:49 Why new parents get sold products they do not need
6:41 Why she calls the fear around AI fearmongering
8:04 What her 11-year-old said after three hours of AI training
8:44 Pay for the subscription, or your knowledge leaks out
11:21 Insurers deny with AI, so one doctor answered with AI
12:02 The colleague who adopts AI is the one who overtakes you
13:37 Why medical training is built for the 1 percent, not the 90 percent
15:37 What COVID-19 showed her about what a machine cannot do
17:36 Take home messages

About this episode:
Sonal Patel is a pediatrician and neonatologist who runs a home health clinic for families in the fourth trimester, the three months after birth she says almost no clinician is trained for. Her turn toward AI started when she appeared as a guest on a podcast and later learned the host, a founder in Silicon Valley, had used her expertise to shape an AI of her own. Patel built her own tool instead, scoped only to the fourth trimester rather than all of pediatrics, and had OB/GYNs and perinatal mental health therapists test it before launch. She says the conversations it has handled have saved families $880 in medical care. Her argument is that roughly 80 percent of medicine is education, that the education is built on physicians' own work, and that insurers and tech companies are already building on it. Her practical advice is unglamorous: start on a paid subscription so your knowledge stays behind a firewall, treat the tool the way you once treated learning the stethoscope, and stop assuming the barrier to entry is bigger than it is. She points to insurers using AI for claim denials as the cautionary tale, and argues the physician most at risk is not the one facing a machine but the one whose colleague learned it first. On replacement, she holds that clinical critical thinking, the kind training drills for the 1 percent of cases that break the pattern, is what AI still depends on, and she closes on what COVID-19 showed her about the part of medicine a machine cannot reach.

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