How LLMs Think Like Clinicians (dochobbs.github.io)

Recent insights reveal striking parallels between large language models (LLMs) and clinical reasoning, emphasizing how understanding these similarities can enhance both AI tool usage and diagnostic practice. At their core, both LLMs and clinicians employ Bayesian reasoning to predict outcomes based on preceding inputs—whether it’s generating the next word in a sentence or determining a patient’s diagnosis. The quality of input, as demonstrated through structured histories in clinical settings, greatly influences the quality of output, underscoring the importance of precise prompt engineering for both systems. The technical implications are significant: just as LLMs are pre-trained on broad datasets before fine-tuning for specific tasks, medical professionals undergo general education before specializing in a field. Techniques like few-shot learning spotlight how both LLMs and learners adapt by recognizing patterns from examples, while emphasizing the need for effective scaffolding in AI and clinical education alike. The shared challenges of hallucination and cognitive biases, known as "epistemic insouciance," further highlight our responsibility to ensure independent verification and diverse exposure to mitigate risks of misinformation in both the AI and medical domains. Understanding these parallels not only fosters better AI integration in healthcare but also enhances clinical education and patient outcomes.