Filling in the Clinical Gaps in Benchmark: Case for HealthBench for the Japanese medical system
- URL: http://arxiv.org/abs/2509.17444v2
- Date: Fri, 10 Oct 2025 10:00:56 GMT
- Title: Filling in the Clinical Gaps in Benchmark: Case for HealthBench for the Japanese medical system
- Authors: Shohei Hisada, Endo Sunao, Himi Yamato, Shoko Wakamiya, Eiji Aramaki,
- Abstract summary: This study investigates the applicability of HealthBench to the Japanese context.<n> resources in Japanese are scarce and often consist of translated multiple-choice questions.
- Score: 5.7880565661958565
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This study investigates the applicability of HealthBench, a large-scale, rubric-based medical benchmark, to the Japanese context. Although robust evaluation frameworks are essential for the safe development of medical LLMs, resources in Japanese are scarce and often consist of translated multiple-choice questions. Our research addresses this issue in two ways. First, we establish a performance baseline by applying a machine-translated version of HealthBench's 5,000 scenarios to evaluate two models: a high-performing multilingual model (GPT-4.1) and a Japanese-native open-source model (LLM-jp-3.1). Secondly, we use an LLM-as-a-Judge approach to systematically classify the benchmark's scenarios and rubric criteria. This allows us to identify 'contextual gaps' where the content is misaligned with Japan's clinical guidelines, healthcare systems or cultural norms. Our findings reveal a modest performance drop in GPT-4.1 due to rubric mismatches, as well as a significant failure in the Japanese-native model, which lacked the required clinical completeness. Furthermore, our classification shows that, despite most scenarios being applicable, a significant proportion of the rubric criteria require localisation. This work underscores the limitations of direct benchmark translation and highlights the urgent need for a context-aware, localised adaptation, a "J-HealthBench", to ensure the reliable and safe evaluation of medical LLMs in Japan.
Related papers
- MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations [4.451052650309736]
We introduce MedErrBench, the first multilingual benchmark for error detection, localization, and correction.<n>Based on an expanded taxonomy of ten common error types, MedErrBench covers English, Arabic and Chinese.<n>Results reveal notable performance gaps, particularly in non-English settings.
arXiv Detail & Related papers (2026-02-05T14:18:20Z) - JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models [47.20100799532625]
We introduce JMedEthicBench, the first multi-turn conversational benchmark for evaluating medical safety of Large Language Models.<n>Using a dual-LLM scoring protocol, we evaluate 27 models and find that commercial models maintain robust safety while medical-specialized models exhibit increased vulnerability.
arXiv Detail & Related papers (2026-01-04T18:18:18Z) - MORQA: Benchmarking Evaluation Metrics for Medical Open-Ended Question Answering [11.575146661047368]
We introduce MORQA, a new multilingual benchmark designed to assess the effectiveness of NLG evaluation metrics.<n>We benchmark both traditional metrics and large language model (LLM)-based evaluators, such as GPT-4 and Gemini.<n>Our results provide the first comprehensive, multilingual qualitative study of NLG evaluation in the medical domain.
arXiv Detail & Related papers (2025-09-15T19:51:57Z) - Rethinking Evidence Hierarchies in Medical Language Benchmarks: A Critical Evaluation of HealthBench [0.0]
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.
arXiv Detail & Related papers (2025-07-31T18:16:10Z) - 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) - LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation [58.25892575437433]
evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error.<n>We present LLMEval-Med, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios.
arXiv Detail & Related papers (2025-06-04T15:43:14Z) - A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP [0.5219568203653523]
We present a Japanese domain-specific language model for the pharmaceutical field, developed through continual pretraining on 2 billion Japanese pharmaceutical tokens and 8 billion English biomedical tokens.<n>We introduce three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task designed to assess consistency reasoning between paired statements.
arXiv Detail & Related papers (2025-05-22T13:27:37Z) - 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) - 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) - MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications [2.838746648891565]
We introduce MEDIC, a framework assessing Large Language Models (LLMs) across five critical dimensions of clinical competence.
We apply MEDIC to evaluate LLMs on medical question-answering, safety, summarization, note generation, and other tasks.
Results show performance disparities across model sizes, baseline vs medically finetuned models, and have implications on model selection for applications requiring specific model strengths.
arXiv Detail & Related papers (2024-09-11T14:44:51Z) - MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models [55.215061531495984]
"MedBench" is a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM.
First, MedBench assembles the largest evaluation dataset (300,901 questions) to cover 43 clinical specialties.
Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer remembering.
arXiv Detail & Related papers (2024-06-24T02:25:48Z) - A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models [57.88111980149541]
We introduce Asclepius, a novel Med-MLLM benchmark that assesses Med-MLLMs in terms of distinct medical specialties and different diagnostic capacities.<n>Grounded in 3 proposed core principles, Asclepius ensures a comprehensive evaluation by encompassing 15 medical specialties.<n>We also provide an in-depth analysis of 6 Med-MLLMs and compare them with 3 human specialists.
arXiv Detail & Related papers (2024-02-17T08:04:23Z) - CMB: A Comprehensive Medical Benchmark in Chinese [67.69800156990952]
We propose a localized medical benchmark called CMB, a Comprehensive Medical Benchmark in Chinese.
While traditional Chinese medicine is integral to this evaluation, it does not constitute its entirety.
We have evaluated several prominent large-scale LLMs, including ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical domain.
arXiv Detail & Related papers (2023-08-17T07:51:23Z) - Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of
Code-Mixed Clinical Texts [56.72488923420374]
Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings.
We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
arXiv Detail & Related papers (2022-04-10T21:46:52Z)
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.