mlsanityData to 14 Oct 2026

HealthBranches: Synthesizing Clinically-Grounded Question Answering Datasets via Decision Pathways

arXiv 2508.07308 ↗First posted 10 Aug 2025cs.CL cs.AI cs.IRcited by 1

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

HealthBranches is a novel benchmark dataset for medical Question-Answering (Q&A), specifically designed to evaluate complex reasoning in Large Language Models (LLMs). This dataset is generated through a semi-automated pipeline that transforms explicit decision pathways from medical source into realistic patient cases with associated questions and answers. Covering 4,063 case studies across 17 healthcare topics, each data point is based on clinically validated reasoning chains. HealthBranches supports both open-ended and multiple-choice question formats and uniquely includes the full reasoning path for each Q&A. Its structured design enables robust evaluation of LLMs' multi-step inference capabilities, including their performance in structured Retrieval-Augmented Generation (RAG) contexts. HealthBranches establishes a foundation for the development of more trustworthy, interpretable, and clinically reliable LLMs in high-stakes domains while also serving as a valuable resource for educational purposes.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 39 of its 48 references that are in the map. First posted on arXiv 10 Aug 2025.

Builds on

39 of 48 references.

References · 39

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27 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2025Structured Outputs Enable General-Purpose LLMs to be Medical Experts
  2. 2024Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
  3. 2026CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain
  4. 2024LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models
  5. 2025RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering
  6. 2025MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering
  7. 2024The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA
  8. 2025MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation