A guide to evade hallucinations and maintain reliability when using large language models for medical research: a narrative review

Abstract

Large language models (LLMs) are increasingly prevalent in medical research; however, fundamental limitations in their architecture create inherent reliability challenges, particularly in specialized medical contexts. These limitations stem from autoregressive prediction mechanisms and computational constraints related to undecidability, hindering perfect accuracy. Current mitigation strategies include advanced prompting techniques such as Chain-of-Thought reasoning and Retrieval-Augmented Generation (RAG) frameworks, although these approaches are insufficient to eliminate the core reliability issues. Meta-analyses of human-artificial intelligence collaboration experiments revealed that, although LLMs can augment individual human capabilities, they are most effective in specific contexts allowing human verification. Successful integration of LLMs in medical research requires careful tool selection aligned with task requirements and appropriate verification mechanisms. Evolution of the field indicates a balanced approach combining technological innovation with established expertise, emphasizing human oversight particularly in complex biological systems. This review highlights the importance of understanding the technical limitations of LLMs while maximizing their potential through thoughtful application and rigorous verification processes, ensuring high standards of scientific integrity in medical research.

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