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Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models
Duy Khoa Pham, Bao Quoc Vo
August 25, 2024
arXiv.org
37 citations
Key Takeaways
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Hallucinations in medical AI are a serious safety concern: Language models can generate plausible-sounding but factually incorrect medical information, which poses particular risks in healthcare settings where accuracy directly impacts clinical decision-making and patient safety.
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Multiple techniques exist to reduce hallucinations, but none are perfect: The paper identifies four main approaches—Retrieval-Augmented Generation (RAG), iterative feedback loops, supervised fine-tuning, and prompt engineering—yet emphasizes that these methods "require further adaptation and optimization for the medical domain" rather than being ready-to-use solutions.
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Medical AI has unique demands that generic solutions don't address: Healthcare applications require not just accuracy but also up-to-date information and strict adherence to medical guidelines, making the hallucination problem more complex than in general-purpose AI applications.
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Trustworthy medical AI requires ongoing development: The paper frames hallucination mitigation as "crucial for developing trustworthy AI systems," suggesting that current technology still needs significant work before AI can reliably support clinical decision-making and biomedical research.
Abstract
The rapid advancement of large language models (LLMs) has significantly impacted various domains, including healthcare and biomedicine. However, the phenomenon of hallucination, where LLMs generate outputs that deviate from factual accuracy or context, poses a critical challenge, especially in high-stakes domains. This paper conducts a scoping study of existing techniques for mitigating hallucinations in knowledge-based task in general and especially for medical domains. Key methods covered in the paper include Retrieval-Augmented Generation (RAG)-based techniques, iterative feedback loops, supervised fine-tuning, and prompt engineering. These techniques, while promising in general contexts, require further adaptation and optimization for the medical domain due to its unique demands for up-to-date, specialized knowledge and strict adherence to medical guidelines. Addressing these challenges is crucial for developing trustworthy AI systems that enhance clinical decision-making and patient safety as well as accuracy of biomedical scientific research.