Rethinking Evidence Hierarchies in Medical Language Benchmarks: A Critical Evaluation of HealthBench
On the map: LLM reasoning → Clinical and biomedical applications
Abstract (arXiv)
HealthBench, a benchmark designed to measure the capabilities of AI systems for health better (Arora et al., 2025), has advanced medical language model evaluation through physician-crafted dialogues and transparent rubrics. However, its reliance on expert opinion, rather than high-tier clinical evidence, risks codifying regional biases and individual clinician idiosyncrasies, further compounded by potential biases in automated grading systems. These limitations are particularly magnified in low- and middle-income settings, where issues like sparse neglected tropical disease coverage and region-specific guideline mismatches are prevalent. The unique challenges of the African context, including data scarcity, inadequate infrastructure, and nascent regulatory frameworks, underscore the urgent need for more globally relevant and equitable benchmarks. To address these shortcomings, we propose anchoring reward functions in version-controlled Clinical Practice Guidelines (CPGs) that incorporate systematic reviews and GRADE evidence ratings. Our roadmap outlines "evidence-robust" reinforcement learning via rubric-to-guideline linkage, evidence-weighted scoring, and contextual override logic, complemented by a focus on ethical considerations and the integration of delayed outcome feedback. By re-grounding rewards in rigorously vetted CPGs, while preserving HealthBench's transparency and physician engagement, we aim to foster medical language models that are not only linguistically polished but also clinically trustworthy, ethically sound, and globally relevant.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 11 of its 39 references that are in the map. First posted on arXiv 31 Jul 2025.
Builds on
11 of 39 references.
Closest ancestors · 1
Same direction, newest first
Other references · 10
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
- 2025HealthBench: Evaluating Large Language Models Towards Improved Human Healthcited by 429
- 2024TrustLLM: Trustworthiness in Large Language Modelscited by 285
- 2025MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMscited by 180
- 2025Artificial Intelligence Index Report 2025cited by 64
- 2024AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Datasetcited by 26
- 2024A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial Lookcited by 18
- 2024Large Language Models Reflect Human Citation Patterns with a Heightened Citation Biascited by 17
- 2025FairMedQA: Benchmarking Bias in Large Language Models for Medical Question Answeringcited by 6
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models
- 2025Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models
- 2025BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
- 2026MedProbeBench: Systematic Benchmarking at Deep Evidence Integration for Expert-level Medical Guideline
- 2025Can Large Language Models Match the Conclusions of Systematic Reviews?
- 2025MedBench v4: A Robust and Scalable Benchmark for Evaluating Chinese Medical Language Models, Multimodal Models, and Intelligent Agents
- 2025MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models
- 2025Evaluating Large Language Models for Evidence-Based Clinical Question Answering