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November Session: Clinical Decision Support for Hu ...
Clinical Decision Support for Human-AI Collaborati ...
Clinical Decision Support for Human-AI Collaboration
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Video Transcription
Video Summary
In this comprehensive webinar, Dr. Adam Rodman, an internist and AI researcher at Harvard Medical School and Google DeepMind, explores the evolving role of artificial intelligence (AI), particularly large language models (LLMs), in clinical reasoning and healthcare. He discusses the complex cognitive processes doctors use in diagnosis, including historical and psychological models like classification, hypothetical-deductive reasoning, illness scripts, dual-process theory, and distributed cognition. Dr. Rodman highlights how AI has been studied and applied since the 1940s, with early systems like AAP Help and Internist One showing promise in diagnosis but not replacing doctors.<br /><br />Recent advancements in LLMs have dramatically improved AI's ability to solve complex diagnostic challenges, even outperforming humans on traditional clinical pathologic case conferences. However, challenges remain, such as automation bias, cognitive de-skilling, and workflow integration. He stresses that AI is unlikely to replace physicians but rather will function alongside them, with evolving models moving from human-in-the-loop to human-on-the-loop systems.<br /><br />Dr. Rodman emphasizes the importance of regulatory frameworks, payment models, and education to support safe AI adoption in medicine. He encourages professional societies to engage in ongoing physician education and partnerships with tech companies to shape AI's integration, addressing risks and maximizing benefits responsibly. The session underscores that while AI holds transformative potential, the integration process will be gradual and complex, requiring sustained collaboration between clinicians, researchers, and industry.
Keywords
artificial intelligence
large language models
clinical reasoning
diagnosis
dual-process theory
automation bias
human-in-the-loop
healthcare AI integration
physician education
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