Rethinking Evidence Hierarchies in Medical Language Benchmarks: A Critical Evaluation of HealthBench

• URL: http://arxiv.org/abs/2508.00081v1
• Date: Thu, 31 Jul 2025 18:16:10 GMT
• Title: Rethinking Evidence Hierarchies in Medical Language Benchmarks: A Critical Evaluation of HealthBench
• Abstract summary: HealthBench is a benchmark designed to measure the capabilities of AI systems for health better.<n>Its reliance on expert opinion, rather than high-tier clinical evidence, risks codifying regional biases and individual clinician idiosyncrasies.<n>We propose anchoring reward functions in version-controlled Clinical Practice Guidelines that incorporate systematic reviews and GRADE evidence ratings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills [39.43312624965993]
  We introduce a scalable evaluation framework with an expert-aligned hierarchical taxonomy of over 100 atomic, clinically-verifiable Boolean curations.<n>A context-aware adaptive router activates only the relevant auto-weighted subset per query, providing the bottleneck needed for scalable evaluation with expert-aligned quality.
  arXiv  Detail & Related papers  (2026-06-16T17:34:53Z)
• Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation [11.873690830791022]
  We study the problem of evaluating AI-generated therapist-like responses for clinically grounded appropriateness and effectiveness.<n>We introduce FAITH-M, a benchmark annotated with expert-assigned ordinal ratings.<n>We propose CARE, a multi-stage evaluation framework that integrates intra-dialogue context, contrastive retrieval, and knowledge-distilled chain-of-thought reasoning.
  arXiv  Detail & Related papers  (2026-04-07T12:30:54Z)
• QuarkMedBench: A Real-World Scenario Driven Benchmark for Evaluating Large Language Models [8.050059911826338]
  Large Language Models (LLMs) excel on standardized medical exams, but high scores often fail to translate to high-quality responses for real-world medical queries.<n>We introduce QuarkMedBench, an ecologically valid benchmark tailored for real-world medical LLM assessment.
  arXiv  Detail & Related papers  (2026-03-14T01:51:43Z)
• AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
  We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
  arXiv  Detail & Related papers  (2026-01-23T11:59:13Z)
• Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models [48.95516224614331]
  We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation.<n>Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical, and implicit adherence to safety protocols.
  arXiv  Detail & Related papers  (2026-01-11T02:20:40Z)
• PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics [35.52940216380734]
  In mental health, clinically inadequate refusals can be perceived as unempathetic and discourage help-seeking.<n>To address this gap, we move beyond refusal-centric metrics and introduce textttPsychEthicsBench, the first principle-grounded benchmark based on Australian psychology and psychiatry guidelines.<n> Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness.
  arXiv  Detail & Related papers  (2026-01-07T04:49:02Z)
• MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs [7.2159153945746795]
  Existing evaluations either test factual medical knowledge in isolation or assess patient-level reasoning without verifying correctness, leaving a critical gap.<n>We introduce MediEval, a benchmark that links MIMIC-IV electronic health records to a unified knowledge base built from UMLS and other biomedical vocabularies.<n> MediEval generates diverse factual and counterfactual medical statements within real patient contexts, enabling systematic evaluation across a 4-quadrant framework.
  arXiv  Detail & Related papers  (2025-12-23T22:52:24Z)
• MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning [52.064286116035134]
  We develop MedAlign, a framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA)<n>We first propose a multimodal Direct Preference Optimization (mDPO) objective to align preference learning with visual context.<n>We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM.
  arXiv  Detail & Related papers  (2025-10-24T02:11:05Z)
• Evaluating Large Language Models for Evidence-Based Clinical Question Answering [4.101088122511548]
  Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications.<n>We curate a benchmark drawing from Cochrane systematic reviews and clinical guidelines.<n>We observe consistent performance patterns across sources and clinical domains.
  arXiv  Detail & Related papers  (2025-09-13T15:03:34Z)
• Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana [0.0]
  This paper presents a survey-based evaluation and insights from Nepal and Ghana.<n>We highlight major obstacles such as data privacy, reliability, and trust issues.<n>We propose a draft Responsible AI (RAI) Framework tailored to resourceconstrained environments.
  arXiv  Detail & Related papers  (2025-08-17T14:59:19Z)
• Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models [46.81512544528928]
  We introduce MedCheck, the first lifecycle-oriented assessment framework specifically designed for medical benchmarks.<n>Our framework deconstructs a benchmark's development into five continuous stages, from design to governance, and provides a comprehensive checklist of 46 medically-tailored criteria.<n>Our analysis uncovers widespread, systemic issues, including a profound disconnect from clinical practice, a crisis of data integrity due to unmitigated contamination risks, and a systematic neglect of safety-critical evaluation dimensions like model robustness and uncertainty awareness.
  arXiv  Detail & Related papers  (2025-08-06T11:11:40Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens [0.609562679184219]
  Existing medical LLM benchmarks largely reflect examination syllabi and disease profiles from high income settings.<n>Alama Health QA was developed using a retrieval augmented generation framework anchored on the Kenyan Clinical Practice Guidelines.<n>Alama scored highest for relevance and guideline alignment; PubMedQA lowest for clinical utility.
  arXiv  Detail & Related papers  (2025-07-22T08:05:30Z)
• Retrieval-Augmented Clinical Benchmarking for Contextual Model Testing in Kenyan Primary Care: A Methodology Paper [0.609562679184219]
  Large Language Models (LLMs) hold promise for improving healthcare access in low-resource settings, but their effectiveness in African primary care remains underexplored.<n>We present a methodology for creating a benchmark dataset and evaluation framework focused on Kenyan Level 2 and 3 clinical care.<n>Our approach uses retrieval augmented generation (RAG) to ground clinical questions in Kenya's national guidelines, ensuring alignment with local standards.
  arXiv  Detail & Related papers  (2025-07-19T13:25:26Z)
• Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models [52.2001050216955]
  Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning.<n>We propose an expert-in-the-loop framework named Expert-Controlled-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training.
  arXiv  Detail & Related papers  (2025-07-12T09:03:30Z)
• DeepSeek in Healthcare: A Survey of Capabilities, Risks, and Clinical Applications of Open-Source Large Language Models [4.506083131558209]
  DeepSeek-R1 is a cutting-edge open-source large language model (LLM) developed by DeepSeek.<n>Released under the permissive MIT license, DeepSeek-R1 offers a transparent and cost-effective alternative to proprietary models.<n>It excels in structured problem-solving domains such as mathematics, healthcare diagnostics, code generation, and pharmaceutical research.
  arXiv  Detail & Related papers  (2025-06-02T02:17:04Z)
• Med-CoDE: Medical Critique based Disagreement Evaluation Framework [72.42301910238861]
  The reliability and accuracy of large language models (LLMs) in medical contexts remain critical concerns.<n>Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance.<n>We propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges.
  arXiv  Detail & Related papers  (2025-04-21T16:51:11Z)
• Contextual Embedding-based Clustering to Identify Topics for Healthcare Service Improvement [3.9726806016869936]
  This study explores unsupervised methods to extract meaningful topics from 439 survey responses collected from a healthcare system in Wisconsin, USA.<n>A keyword-based filtering approach was applied to isolate complaint-related feedback using a domain-specific lexicon.<n>To improve coherence and interpretability where data are scarce and consist of short-texts, we propose kBERT.
  arXiv  Detail & Related papers  (2025-04-18T20:38:24Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation [31.061600616994145]
  HDCEval is built on a set of fine-grained medical evaluation guidelines developed in collaboration with professional doctors.<n>The framework decomposes complex evaluation tasks into specialized subtasks, each evaluated by expert models.<n>This hierarchical approach ensures that each aspect of the evaluation is handled with expert precision, leading to a significant improvement in alignment with human evaluators.
  arXiv  Detail & Related papers  (2025-01-12T07:30:49Z)
• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding [72.18719355481052]
  We introduce a novel task called Medical Report Grounding (MRG)<n>MRG aims to directly identify diagnostic phrases and their corresponding grounding boxes from medical reports in an end-to-end manner.<n>We propose uMedGround, a robust and reliable framework that leverages a multimodal large language model to predict diagnostic phrases.
  arXiv  Detail & Related papers  (2024-04-10T07:41:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.