Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI

TL;DR

This paper introduces a large, expert-validated clinical reasoning dataset for medical AI, created through a hybrid human-LLM pipeline to enhance transparency and trustworthiness in medical question-answering systems.

Contribution

It presents a novel, large-scale, expert-validated dataset with chain-of-thought explanations, curated via a scalable hybrid pipeline for medical AI research.

Findings

The dataset contains 31,247 validated medical QA pairs with explanations.

Expert review improves the quality and clinical relevance of LLM-generated rationales.

The dataset supports development of transparent and trustworthy medical AI models.

Abstract

Despite strong performance in medical question-answering, the clinical adoption of Large Language Models (LLMs) is critically hampered by their opaque 'black-box' reasoning, limiting clinician trust. This challenge is compounded by the predominant reliance of current medical LLMs on corpora from scientific literature or synthetic data, which often lack the granular expert validation and high clinical relevance essential for advancing their specialized medical capabilities. To address these critical gaps, we introduce a highly clinically relevant dataset with 31,247 medical question-answer pairs, each accompanied by expert-validated chain-of-thought (CoT) explanations. This resource, spanning multiple clinical domains, was curated via a scalable human-LLM hybrid pipeline: LLM-generated rationales were iteratively reviewed, scored, and refined by medical experts against a structured rubric, with substandard outputs revised through human effort or guided LLM regeneration until expert consensus. This publicly available dataset provides a vital source for the development of medical LLMs that capable of transparent and verifiable reasoning, thereby advancing safer and more interpretable AI in medicine.