Understanding the Inner Workings of Large Language Models in Medicine

Abstract

Background: Large language models (LLMs) are increasingly influencing medical practice, education, and research. Their responsible integration into healthcare requires expertise in medical, ethical, practical, and theoretical domains. Objectives: This article examines how theoretical knowledge of LLMs and their internal mechanisms enhances the interpretation of model outputs in medical contexts. Methods: We prompted GPT-o1 to generate examples illustrating how understanding transformer architecture can facilitate output interpretation. Key topics were extracted from its responses, and illustrative cases were validated using Consensus.app, an AI-based web-search tool. Results: Five key topics were identified: (1) anticipating contextual focus in medical reasoning, (2) explaining “generic” or “textbook” responses, (3) understanding strengths and weaknesses in differential diagnosis, (4) explaining ambiguous or contradictory responses, and (5) identifying hallucinations in unfamiliar scenarios. Case examples highlight both benefits and limitations, including accurate attention to salient clinical details, reliance on generalized patterns, risks of base rate neglect in differential diagnosis, challenges of ambiguous prompts, and hallucinations in rare or underrepresented cases. Conclusions: A theoretical understanding of LLMs is crucial for responsible clinical integration. Distinguishing between well-represented (short head) and underrepresented (long tail) knowledge, recognizing generic responses, and identifying hallucinations are essential competencies. Coupled with medical and ethical expertise, these skills will enable healthcare professionals to leverage LLMs effectively while mitigating risks.

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