JAMA+ AI Conversations Multiple Reasoning Models and the Future of AI Chatbots
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Dec 30, 2025 Jonathan Chen, an Associate Professor of Medicine at Stanford, and Ethan Goh, Executive Director of the Arise Research Network, dive into the transformative landscape of AI chatbots. They discuss innovative reasoning models that enhance complex decision-making in healthcare. The duo highlights the importance of training clinicians to utilize AI effectively, while also addressing risks like automation bias. They explore why chatbots tend to agree with users and stress the need for an educational framework that prepares the next generation for AI's challenges.
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Reasoning Models Change AI Capabilities
- Multiple-reasoning models let AI 'talk to itself' and run chain-of-thought processes for harder problems.
- Jonathan Chen notes these models can outperform prior models on complex clinical reasoning tasks.
Longer Replies Can Mean Deeper Reasoning
- Reinforcement learning plus internal reasoning often improves performance on nuanced tasks.
- Ethan Goh and Jonathan Chen describe longer response times as the model 'thinking' and checking sources.
Train Clinicians To Use AI Effectively
- Train clinicians to use AI interactively rather than treat it as a search engine.
- Jonathan Chen's study shows trained doctors working with AI perform better than untrained peers.