Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
arXiv:2403.04890v4 Announce Type: replace Abstract: In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios, along with clinician-approved reasoned answers. Additionally, we implement a prompt driven by Chain of Thought (CoT) reasoning, CLINICR, to mirror the prospective process of incremental reasoning, reaching a correct response to medical questions. We empirically demonstrate how CLINICR outperforms the state-of-the-art 5-shot CoT-based prompt (Li\'evin et al., 2022).
The rapid advancement of LLMs is pushing the boundaries of their application in complex domains like medicine, necessitating more sophisticated prompting and reasoning capabilities.
This development indicates progress towards more reliable and nuanced AI application in professional fields, particularly for high-stakes decision support where accuracy and explainability are paramount.
The ability to prompt LLMs for open-ended medical questions with clinician-approved reasoned answers, outperforming existing methods, marks a step towards AI that can more effectively mimic expert human thought processes in medicine.
- · AI developers
- · Healthcare sector (diagnostics)
- · Patients
- · Medical AI research institutions
- · LLM developers without strong reasoning capabilities
- · Traditional medical knowledge bases
- · Purely statistical AI models
Improved diagnostic tools and medical education platforms leveraging advanced LLM reasoning.
Increased trust and adoption of AI assistants by medical professionals, leading to workflow integration.
Ethical and regulatory frameworks for AI in highly sensitive domains will need to rapidly evolve to accommodate these capabilities.
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