Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
This work addresses the need for explainable AI in medical LLMs to increase trust and integration in healthcare, though it is incremental as it adapts existing concepts and surveys methods.
The paper tackles the lack of studies on reasoning behavior in medical Large Language Models (LLMs), emphasizing its importance for explainable AI in healthcare, and proposes theoretical frameworks and surveys current approaches to enhance transparency.
Background: Despite the current ubiquity of Large Language Models (LLMs) across the medical domain, there is a surprising lack of studies which address their reasoning behaviour. We emphasise the importance of understanding reasoning behaviour as opposed to high-level prediction accuracies, since it is equivalent to explainable AI (XAI) in this context. In particular, achieving XAI in medical LLMs used in the clinical domain will have a significant impact across the healthcare sector. Results: Therefore, in this work, we adapt the existing concept of reasoning behaviour and articulate its interpretation within the specific context of medical LLMs. We survey and categorise current state-of-the-art approaches for modeling and evaluating reasoning reasoning in medical LLMs. Additionally, we propose theoretical frameworks which can empower medical professionals or machine learning engineers to gain insight into the low-level reasoning operations of these previously obscure models. We also outline key open challenges facing the development of Large Reasoning Models. Conclusion: The subsequent increased transparency and trust in medical machine learning models by clinicians as well as patients will accelerate the integration, application as well as further development of medical AI for the healthcare system as a whole.