Clinical Language Model Evaluation

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Clinical language model evaluation asks whether models can answer medical questions, interpret records, support conversations, and assist with decisions without causing harm. Researchers use exam-style benchmarks, clinician-authored rubrics, electronic health records, simulated consultations, and tests that vary language, missing information, patient behavior, or evidence; they also compare expert ratings with automated judges. Evaluation increasingly measures safety, omissions, evidence grounding, uncertainty, consistency, and workflow performance alongside accuracy. A central challenge is that strong scores on static tests or plausible reasoning may not transfer to dynamic, incomplete clinical encounters, while automated judges can disagree with clinicians and require validation.

Latest papers

419 papers49 papers in the last four weeks, up 2% on the four weeks before. 0.3% of all new papers.

Feb 26, 2026

Decomposing Physician Disagreement in HealthBench

We decompose physician disagreement in the HealthBench medical AI evaluation dataset to understand where variance resides and what observable features can explain it. Rubric identity accounts for 15.8% of met/not-met label variance but only 3.6-6.9% of disagreement variance; physician identity accounts for just 2.4%. The dominant 81.8% case-level residual is not reduced by HealthBench's metadata labels (z = -0.22, p = 0.83), normative rubric language (pseudo R^2 = 1.2%), medical specialty (0/300 Tukey pairs significant), surface-feature triage (AUC = 0.58), or embeddings (AUC = 0.485). Disagreement follows an inverted-U with completion quality (AUC = 0.689), confirming physicians agree on clearly good or bad outputs but split on borderline cases. Physician-validated uncertainty categories reveal that reducible uncertainty (missing context, ambiguous phrasing) more than doubles disagreement odds (OR = 2.55, p < 10^(-24)), while irreducible uncertainty (genuine medical ambiguity) has no effect (OR = 1.01, p = 0.90), though even the former explains only ~3% of total variance. The agreement ceiling in medical AI evaluation is thus largely structural, but the reducible/irreducible dissociation suggests that closing information gaps in evaluation scenarios could lower disagreement where inherent clinical ambiguity does not, pointing toward actionable evaluation design improvements.

Satya Borgohain, Roy Mariathas

Annotator Disagreement Clinical Language Model Evaluation Rubric-Based Evaluation

Feb 25, 2026

Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs

Effectively addressing client resistance is a sophisticated clinical skill in psychological counseling, yet practitioners often lack timely and scalable supervisory feedback to refine their approaches. Although current NLP research has examined overall counseling quality and general therapeutic skills, it fails to provide granular evaluations of high-stakes moments where clients exhibit resistance. In this work, we present a comprehensive pipeline for the multi-dimensional evaluation of human counselors' interventions specifically targeting client resistance in text-based therapy. We introduce a theory-driven framework that decomposes counselor responses into four distinct communication mechanisms. Leveraging this framework, we curate and share an expert-annotated dataset of real-world counseling excerpts, pairing counselor-client interactions with professional ratings and explanatory rationales. Using this data, we perform full-parameter instruction tuning on a Llama-3.1-8B-Instruct backbone to model fine-grained evaluative judgments of response quality and generate explanations underlying. Experimental results show that our approach can effectively distinguish the quality of different communication mechanisms (77-81% F1), substantially outperforming GPT-4o and Claude-3.5-Sonnet (45-59% F1). Moreover, the model produces high-quality explanations that closely align with expert references and receive near-ceiling ratings from human experts (2.8-2.9/3.0). A controlled experiment with 43 counselors further confirms that receiving these AI-generated feedback significantly improves counselors' ability to respond effectively to client resistance.

Anqi Li, Ruihan Wang, Zhaoming Chen +5

Zhejiang University · Westlake University, Hangzhou, China

Mental Health Counseling Clinical Language Model Evaluation Explainability Evaluation

Feb 24, 2026

Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source small language models (SLMs) -- Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-1.5B-Instruct, and Gemma-3-1B-it -- for binary extraction of 13 clinical features from 1,221 anonymized Persian transcripts collected at a cancer palliative care call center. Using a few-shot prompting strategy without fine-tuning, models were assessed on macro-averaged F1-score, Matthews Correlation Coefficient (MCC), sensitivity, and specificity to account for class imbalance. Qwen2.5-7B-Instruct achieved the highest overall performance (median macro-F1: 0.899; MCC: 0.797), while Gemma-3-1B-it showed the weakest results. Larger models (7B--8B parameters) consistently outperformed smaller counterparts in sensitivity and MCC. A bilingual analysis of Aya-expanse-8B revealed that translating Persian transcripts to English improved sensitivity, reduced missing outputs, and boosted metrics robust to class imbalance, though at the cost of slightly lower specificity and precision. Feature-level results showed reliable extraction of physiological symptoms across most models, whereas psychological complaints, administrative requests, and complex somatic features remained challenging. These findings establish a practical, privacy-preserving blueprint for deploying open-source SLMs in multilingual clinical NLP settings with limited infrastructure and annotation resources, and highlight the importance of jointly optimizing model scale and input language strategy for sensitive healthcare applications.

Mohammadreza Ghaffarzadeh-Esfahani, Nahid Yousefian, Ebrahim Heidari-Farsani +4

Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran · Department of Emergency Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Clinical Information Extraction Small Language Models Clinical Language Model Evaluation Low-Resource Language Processing Privacy-Preserving Language Models

Feb 23, 2026

Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming

Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the complex, longitudinal risks inherent in therapeutic dialogue. We introduce an evaluation framework that pairs AI psychotherapists with simulated patient agents equipped with dynamic cognitive-affective models and assesses therapy session simulations against a comprehensive quality of care and risk ontology. We apply this framework to a high-impact test case, Alcohol Use Disorder, evaluating six AI agents (including ChatGPT, Gemini, and Character AI) against a clinically-validated cohort of 15 patient personas representing diverse clinical phenotypes. Our large-scale simulation (N=369 sessions) reveals critical safety gaps in the use of AI for mental health support. We identify specific iatrogenic risks, including the validation of patient delusions ("AI Psychosis") and failure to de-escalate suicide risk. Finally, we validate an interactive data visualization dashboard with diverse stakeholders, including AI engineers and red teamers, mental health professionals, and policy experts (N=9), demonstrating that this framework effectively enables stakeholders to audit the "black box" of AI psychotherapy. These findings underscore the critical safety risks of AI-provided mental health support and the necessity of simulation-based clinical red teaming before deployment.

Ian Steenstra, Paola Pedrelli, Weiyan Shi +2

Northeastern University Boston, MA, USA · Harvard Medical School Boston, MA, USA

LLM Safety Evaluation Clinical Language Model Evaluation Medical LLMs LLM Red Teaming Large Language Model-Based Role-Play Simulation

Feb 23, 2026

TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots

Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference tests, and generic dialogue benchmarks--fail to capture the clinically critical dimensions of psychotherapy. We introduce THERAPYGYM, a framework that evaluates and improves therapy chatbots along two clinical pillars: fidelity and safety. Fidelity is measured using the Cognitive Therapy Rating Scale (CTRS), implemented as an automated pipeline that scores adherence to CBT techniques over multi-turn sessions. Safety is assessed using a multi-label annotation scheme, covering therapy-specific risks (e.g., failing to address harm or abuse). To mitigate bias and unreliability in LLM-based judges, we further release THERAPYJUDGEBENCH, a validation set of 116 dialogues with 1,270 expert ratings for auditing and calibration against licensed clinicians. THERAPYGYM also serves as a training harness: CTRS and safety-based rewards drive RL with configurable patient simulations spanning diverse symptom profiles. Models trained in THERAPYGYM improve on expert ratings, with average CTRS rising from 0.10 to 0.60 (and 0.16 to 0.59 under LLM judges). Our work enables scalable development of therapy chatbots that are faithful to evidence-based practice and safer in high-stakes use.

Fangrui Huang, Souhad Chbeir, Arpandeep Khatua +8

Department of Computer Science, Stanford University, Stanford, CA, USA · Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA · The University of Hong Kong, Hong Kong +2

Mental Health Conversational Agents Clinical Language Model Evaluation Conversational Agents LLM Safety Benchmarks Cognitive Behavioral Therapy

Feb 18, 2026

LiveClin: A Live Clinical Benchmark without Leakage

The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address these challenges, we introduce LiveClin, a live benchmark designed for approximating real-world clinical practice. Built from contemporary, peer-reviewed case reports and updated biannually, LiveClin ensures clinical currency and resists data contamination. Using a verified AI-human workflow involving 239 physicians, we transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway. The benchmark currently comprises 1,407 case reports and 6,605 questions. Our evaluation of 26 models on LiveClin reveals the profound difficulty of these real-world scenarios, with the top-performing model achieving a Case Accuracy of just 35.7%. In benchmarking against human experts, Chief Physicians achieved the highest accuracy, followed closely by Attending Physicians, with both surpassing most models. LiveClin thus provides a continuously evolving, clinically grounded framework to guide the development of medical LLMs towards closing this gap and achieving greater reliability and real-world utility. Our data and code are publicly available at https://github.com/AQ-MedAI/LiveClin.

Xidong Wang, Shuqi Guo, Yue Shen +6

The Chinese University of Hong Kong, ShenZhen · Ant Healthcare, Ant Group · The Ohio State University +1

Clinical Language Model Evaluation Medical LLMs Benchmark Design Clinical Reasoning Multimodal Model Evaluation

Feb 17, 2026

Multi-Objective Alignment of Language Models for Personalized Psychotherapy

Mental health disorders affect over 1 billion people worldwide, yet access to care remains limited by workforce shortages and cost constraints. While AI systems show therapeutic promise, current alignment approaches optimize objectives independently, failing to balance patient preferences with clinical safety. We survey 335 individuals with lived mental health experience to collect preference rankings across therapeutic dimensions, then develop a multi-objective alignment framework using direct preference optimization. We train reward models for six criteria -- empathy, safety, active listening, self-motivated change, trust/rapport, and patient autonomy -- and systematically compare multi-objective approaches against single-objective optimization, supervised fine-tuning, and parameter merging. Multi-objective DPO (MODPO) achieves superior balance (77.6% empathy, 62.6% safety) compared to single-objective optimization (93.6% empathy, 47.8% safety), and therapeutic criteria outperform general communication principles by 17.2%. Blinded clinician evaluation confirms MODPO is consistently preferred, with LLM-evaluator agreement comparable to inter-clinician reliability.

Mehrab Beikzadeh, Yasaman Asadollah Salmanpour, Ashima Suvarna +4

Department of Computer Science, University of California, Los Angeles, CA, USA · Department of Psychology, The University of Texas at Austin, Austin, TX, USA · Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA

Personalized Language Model Alignment Multi-Objective Language Model Alignment Clinical Language Model Evaluation Direct Preference Optimization

Feb 17, 2026

Evidence-Grounded Subspecialty Reasoning: Evaluating a Curated Clinical Intelligence Layer on the 2025 Endocrinology Board-Style Examination

Background: Large language models have demonstrated strong performance on general medical examinations, but subspecialty clinical reasoning remains challenging due to rapidly evolving guidelines and nuanced evidence hierarchies. Methods: We evaluated January Mirror, an evidence-grounded clinical reasoning system, against frontier LLMs (GPT-5, GPT-5.2, Gemini-3-Pro) on a 120-question endocrinology board-style examination. Mirror integrates a curated endocrinology and cardiometabolic evidence corpus with a structured reasoning architecture to generate evidence-linked outputs. Mirror operated under a closed-evidence constraint without external retrieval. Comparator LLMs had real-time web access to guidelines and primary literature. Results: Mirror achieved 87.5% accuracy (105/120; 95% CI: 80.4-92.3%), exceeding a human reference of 62.3% and frontier LLMs including GPT-5.2 (74.6%), GPT-5 (74.0%), and Gemini-3-Pro (69.8%). On the 30 most difficult questions (human accuracy less than 50%), Mirror achieved 76.7% accuracy. Top-2 accuracy was 92.5% for Mirror versus 85.25% for GPT-5.2. Conclusions: Mirror provided evidence traceability: 74.2% of outputs cited at least one guideline-tier source, with 100% citation accuracy on manual verification. Curated evidence with explicit provenance can outperform unconstrained web retrieval for subspecialty clinical reasoning and supports auditability for clinical deployment.

Amir Hosseinian, MohammadReza Zare Shahneh, Umer Mansoor +3

January AI · Stanford University

Clinical Language Model Evaluation Evidence-Grounded Reasoning Clinical Reasoning Clinical QA Evidence-Grounded Generation

Feb 16, 2026

Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes

Clinical documentation can contain emotionally charged language with stigmatizing or privileging valences. We present a framework for detecting and classifying such language as stigmatizing, privileging, or neutral. We constructed a curated lexicon of biased terms scored for emotional valence. We then used lexicon-based matching to extract text chunks from OB-GYN delivery notes (Mount Sinai Hospital, NY) and MIMIC-IV discharge summaries across multiple specialties. Three clinicians annotated all chunks, enabling characterization of valence patterns across specialties and healthcare systems. We benchmarked multiple classification strategies (zero-shot prompting, in-context learning, and supervised fine-tuning) across encoder-only models (GatorTron) and generative large language models (Llama). Fine-tuning with lexically primed inputs consistently outperformed prompting approaches. GatorTron achieved an F1 score of 0.96 on the OB-GYN test set, outperforming larger generative models while requiring minimal prompt engineering and fewer computational resources. External validation on MIMIC-IV revealed limited cross-domain generalizability (F1 < 0.70, 44% drop). Training on the broader MIMIC-IV dataset improved generalizability when testing on OB-GYN (F1 = 0.71, 11% drop), but at the cost of reduced precision. Our findings demonstrate that fine-tuning outperforms prompting for emotional valence classification and that models must be adapted to specific medical specialties to achieve clinically appropriate performance. The same terms can carry different emotional valences across specialties: words with clinical meaning in one context may be stigmatizing in another. For bias detection, where misclassification risks undermining clinician trust or perpetuating patient harm, specialty-specific fine-tuning is essential to capture these semantic shifts. * Equal contribution.

Isotta Landi, Eugenia Alleva, Nicole Bussola +5

Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, NY. · Icahn School of Medicine at Mount Sinai, NY. · Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, NY. +2

Clinical NLP Clinical Language Model Evaluation

Feb 16, 2026

Assessing Large Language Models for Medical QA: Zero-Shot and LLM-as-a-Judge Evaluation

Recently, Large Language Models (LLMs) have gained significant traction in medical domain, especially in developing a QA systems to Medical QA systems for enhancing access to healthcare in low-resourced settings. This paper compares five LLMs deployed between April 2024 and August 2025 for medical QA, using the iCliniq dataset, containing 38,000 medical questions and answers of diverse specialties. Our models include Llama-3-8B-Instruct, Llama 3.2 3B, Llama 3.3 70B Instruct, Llama-4-Maverick-17B-128E-Instruct, and GPT-5-mini. We are using a zero-shot evaluation methodology and using BLEU and ROUGE metrics to evaluate performance without specialized fine-tuning. Our results show that larger models like Llama 3.3 70B Instruct outperform smaller models, consistent with observed scaling benefits in clinical tasks. It is notable that, Llama-4-Maverick-17B exhibited more competitive results, thus highlighting evasion efficiency trade-offs relevant for practical deployment. These findings align with advancements in LLM capabilities toward professional-level medical reasoning and reflect the increasing feasibility of LLM-supported QA systems in the real clinical environments. This benchmark aims to serve as a standardized setting for future study to minimize model size, computational resources and to maximize clinical utility in medical NLP applications.

Shefayat E Shams Adib, Ahmed Alfey Sani, Ekramul Alam Esham +2

Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh

Medical QA Medical LLMs Clinical Language Model Evaluation LLM Evaluation

Feb 2, 2026

LingLanMedBench: A Unified Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine

Reliable evaluation of large language models in traditional Chinese medicine remains limited because existing benchmarks mainly focus on examination-style question answering or isolated generative tasks. We developed LingLanMedBench, a unified benchmark comprising 25,620 expert-verified items across 13 subtasks and five domains, covering knowledge, language understanding, information extraction, and diagnostic-therapeutic decision-making. LingLanMedBench standardizes task formats and evaluation metrics and further incorporates synonym-tolerant clinical-label matching, empirically derived hard subsets, and decision-recognition reformulations of open-ended clinical tasks. Fourteen open-source and proprietary models were evaluated under a standardized zero-shot protocol. Performance varied substantially across domains, model families, task formulations, and difficulty levels. The leading overall score decreased from 51.1 on the full benchmark to 31.9 on the hard subsets. Prescription performance also differed markedly between open-ended generation, with F1 scores of 32.7 and 20.3, and decision recognition, with accuracies of 90.6 and 56.8 on the full and hard subsets, respectively. These findings reveal a substantial gap between knowledge-oriented performance and clinically faithful reasoning. LingLanMedBench provides a standardized and reproducible framework for characterizing current capabilities, identifying systematic limitations, and tracking meaningful progress in large language models for traditional Chinese medicine.

Rui Hua, Yu Wei, Zixin Shu +17

Department of Computer Science and Technology, Beijing Jiaotong University, Beijing, China · Tianjin Tasly Digital Chinese Medicine Technology Co., Ltd., Tianjin, China · Tasly Biopharmaceuticals Co., Ltd., Tianjin, China +11

Traditional Chinese Medicine Medical LLMs LLM Evaluation Clinical Language Model Evaluation

Jan 25, 2026

Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context

Safety alignment in Large Language Models is critical for healthcare; however, reliance on binary refusal boundaries often results in over-refusal of benign queries or unsafe compliance with harmful ones. While existing benchmarks measure these extremes, they fail to evaluate Safe Completion: the model's ability to maximise helpfulness on dual-use or borderline queries by providing safe, high-level guidance without crossing into actionable harm. We introduce Health-ORSC-Bench, the first large-scale benchmark designed to systematically measure Over-Refusal and Safe Completion quality in healthcare. Comprising 31,920 benign boundary prompts across seven health categories (e.g., self-harm, medical misinformation), our framework uses an automated pipeline with human validation to test models at varying levels of intent ambiguity. We evaluate 30 state-of-the-art LLMs, including GPT-5 and Claude-4, revealing a significant tension: safety-optimised models frequently refuse up to 80% of "Hard" benign prompts, while domain-specific models often sacrifice safety for utility. Our findings demonstrate that model family and size significantly influence calibration: larger frontier models (e.g., GPT-5, Llama-4) exhibit "safety-pessimism" and higher over-refusal than smaller or MoE-based counterparts (e.g., Qwen-3-Next), highlighting that current LLMs struggle to balance refusal and compliance. Health-ORSC-Bench provides a rigorous standard for calibrating the next generation of medical AI assistants toward nuanced, safe, and helpful completions. Furthermore, our benchmark facilitates reproducible evaluation, encourages safety calibration, and supports development of clinically reliable, context-aware, human-aligned medical AI systems. Our code and data are available at: https://github.com/ZhihaoZhang97/Health-ORSC-Bench. Warning: Some contents may include toxic or undesired contents.

Zhihao Zhang, Liting Huang, Guanghao Wu +3

Macquarie University1 · UTS2 · MBZUAI3 +1

LLM Safety Benchmarks Language Model Safety Evaluation Healthcare LLM Refusal LLM Refusal Behavior Over-Refusal+2

Jan 23, 2026

SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care

Large language models (LLMs) deployed in clinical decision support may acquiesce to patient requests for care that conflicts with evidence-based guidelines. We developed SycoEval-EM, a multi-agent simulation framework to evaluate LLM robustness to adversarial patient persuasion in emergency medicine. Across 19 contemporary LLMs and 1,425 simulated clinical encounters spanning three Choosing Wisely scenarios, acquiescence rates ranged from 0% to 100%, revealing a bimodal distribution. Seven models maintained near-perfect guideline adherence, while six acquiesced in the majority of encounters. Vulnerability varied substantially across clinical scenarios. Acquiescence was highest for CT imaging requests, intermediate for antibiotic prescriptions for sinusitis, and lowest for opioid prescriptions for acute back pain. Model scale, recency, and performance on static medical benchmarks did not consistently predict robustness. All five persuasion tactics produced similar acquiescence rates, with no statistically significant differences after correction for multiple comparisons, suggesting a generalized susceptibility rather than tactic-specific weaknesses. LLM-as-judge evaluation was validated against two independent physician raters across 95 matched conversations and demonstrated near-perfect agreement for the primary outcome of acquiescence (Cohens kappa = 0.957). These findings indicate that static medical benchmarks are insufficient to predict safety performance under sustained social pressure and support incorporating multi-turn adversarial testing into clinical AI evaluation. Notably, two models achieved perfect guideline adherence across all encounters, demonstrating that robustness to patient pressure is attainable without sacrificing effective clinical communication.

Dongshen Peng, Yi Wang, Austin Schoeffler +5

UNC Chapel Hill · University of Waterloo · Stanford University

LLM Sycophancy Language Model Safety Evaluation Healthcare Adversarial Robustness of Language Models Clinical Language Model Evaluation Medical LLMs

Jan 22, 2026

Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation

Large-scale language models (LLMs) often offer clinical judgments based on incomplete information, increasing the risk of misdiagnosis. Existing studies have primarily evaluated confidence in single-turn, static settings, overlooking the coupling between confidence and correctness as clinical evidence accumulates during real consultations, which limits their support for reliable decision-making. We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations. Our benchmark unifies three types of medical data for open-ended diagnostic generation and introduces an information sufficiency gradient to characterize the confidence-correctness dynamics as evidence increases. We implement and compare 27 representative methods on this benchmark; two key insights emerge: (1) medical data amplifies the inherent limitations of token-level and consistency-level confidence methods, and (2) medical reasoning must be evaluated for both diagnostic accuracy and information completeness. Based on these insights, we present MedConf, an evidence-grounded linguistic self-assessment framework that constructs symptom profiles via retrieval-augmented generation, aligns patient information with supporting, missing, and contradictory relations, and aggregates them into an interpretable confidence estimate through weighted integration. Across two LLMs and three medical datasets, MedConf consistently outperforms state-of-the-art methods on both AUROC and Pearson correlation coefficient metrics, maintaining stable performance under conditions of information insufficiency and multimorbidity. These results demonstrate that information adequacy is a key determinant of credible medical confidence modeling, providing a new pathway toward building more reliable and interpretable large medical models.

Zhiyao Ren, Yibing Zhan, Siyuan Liang +3

Nanyang Technological University, Singapore, Singapore. · Wuhan University, Wuhan, China.

Healthcare Clinical Reasoning LLM Uncertainty Estimation Clinical Language Model Evaluation Medical LLMs

Jan 6, 2026

MAPLE: Medical Aspect-Based Summarization with Phrase-Level Evidence

Trustworthy clinical summarization requires every claim to be traceable to its evidence, yet existing attribution often resolves only to the sentence or document, leaving clinicians to scan surrounding text for the few words that matter. We argue that the unit of attribution should match the unit of verification: the precise phrase the reader's eye must land on. We present MAPLE (Medical Aspect-Based Summarization with Phrase-Level Evidence), a human-annotated benchmark that grounds each summarized claim in both cited sentences and contributory phrases within them. Spanning 152 randomized controlled trial (RCT) abstracts and 16 clinically motivated aspects, MAPLE comprises 1,799 aspect-based summaries with two-level evidence. We further introduce a decoupled evaluation framework that separately scores content, traceability, and locatability, together with a proxy for the amount of source text a clinician must inspect to verify a claim. Benchmarking eleven LLMs shows that sentence-level citation is consistently strong (C-F1 up to 90.9%), while phrase-level grounding remains less stable and the most discriminative axis across models (P-F1 66.1-84.5%). These results suggest that the key challenge is not only producing accurate summaries, but localizing their supporting evidence precisely enough for efficient clinical verification. Data and code are available at https://github.com/chubohao/maple.

Bohao Chu, Hendrik Damm, Tabea M. G. Pakull +5

University of Duisburg-Essen · University of Applied Sciences and Arts Dortmund · University Hospital Essen +1

Healthcare Text Summarization Summarization Evaluation Evidence-Grounded Generation Clinical Language Model Evaluation

Dec 25, 2025

Context-Aware Classification and Grading of Sensitive Information in Online Conversational Health Data

Online medical consultations contain sensitive health information whose privacy implications depend not only on the entities mentioned but also on how those entities are described in context. Existing classification and grading approaches often map health-information entities directly to predefined sensitivity levels, potentially overlooking whether a condition is confirmed, suspected, negated, hypothetical, or merely planned for investigation. In this study, we formulate sensitive-information grading in online medical dialogues as a context-aware evaluation task. We develop a standard-informed operational framework that incorporates assertion status, experiencer, test-result status, and information granularity. We further design a naturalistic evaluation setting together with contrastive cases that minimally alter negation, uncertainty, experiencer, or granularity, and compare large language models under mention-only and full-context conditions. The study aims to quantify the contribution of contextual information to sensitivity grading and to characterize safety-critical over- and under-grading errors. Our framework provides a reproducible basis for evaluating whether LLMs can distinguish sensitive entity mentions from contextually established sensitive disclosures.

Yiwei Yan, Guanfeng Liu

School of Computing, Macquarie University, Australia

Healthcare Text Classification LLM Safety Evaluation Clinical Language Model Evaluation

Dec 1, 2025

First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations

Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a 1,100-task benchmark of primary care-to-specialist consultation cases to measure the frequency and severity of potentially harmful errors from LLM-generated medical consultation recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 20 notable LLMs and 4 widely used retrieval-augmented generation (RAG) clinical AI tools, direct application of recommendations carried potential for severe harm in up to 24.6% of cases, with errors of omission accounting for more than 80% of severe errors. Harm potential was not uniform across systems, with clinical AI tools outperforming generalist LLMs, and multi-agent AI teaming further improving performance in generalist models. In a randomized study of 101 U.S.-licensed generalist physicians, AI assistance improved physician performance compared to conventional resources. However, AI-assisted physicians frequently omitted valuable AI-generated recommendations and still scored lower than many AI systems alone. Had those recommendations been incorporated, combined human-AI responses would have outperformed both the human and AI system as used, suggesting complementary strengths and unrealized potential in human-AI teaming. Collectively, these results show that despite strong performance on medical knowledge benchmarks, widely used AI tools can produce medical consultation advice with the potential for severe harm, and highlight the need for explicit measurement of clinical safety. The benchmark and leaderboard are publicly available to support ongoing evaluation and improvement of AI systems used for clinical care.

David Wu, Fateme Nateghi Haredasht, Saloni Kumar Maharaj +54

Healthcare Clinical Decision Support Human-in-the-Loop AI AI Safety Evaluation Clinical Language Model Evaluation

Nov 20, 2025

Algorithmic Reflectivity in Large Language Models: Methodological Study of Linguistic Structure in Mentalization

Mentalization is a core human capacity involving the interpretation of one's own and others' behavior in terms of underlying mental states. Large language models (LLMs) can generate linguistically reflective discourse, raising the question of whether the formal linguistic structure of mentalization can be reproduced without experiential and affective processes. This study assessed whether an LLM can generate mentalization profiles structurally coherent with the dimensions of mentalization-based treatment (MBT) and recognized as formally mentalizing by expert clinicians. Fifty human-model dialogues were generated with GPT-4.1 under standardized conditions; at the end of each, the model produced a narrative mentalization profile. Five MBT-trained psychiatrists independently rated each profile on 4 canonical MBT axes and 2 higher-order dimensions (regulatory functions, synthetic profile) for evaluative and argumentative coherence. Interrater reliability was assessed with ICC(3,k). Mean scores ranged from 3.63 to 3.98, indicating moderate-to-high structural coherence; ICC(3,k) in the evaluative domain ranged from 0.60 to 0.84. Evaluative and argumentative coherence were strongly associated (Pearson r 0.826-0.967). A negative control with mismatched dialogue-profile pairs yielded minimal ratings. Clinicians described outputs as linguistically organized and clinically interpretable, but with limited affective variability and contextual integration. The findings suggest that LLMs can reproduce the formal linguistic structure of reflective discourse - a phenomenon we term algorithmic reflectivity - which appears more consistent with structural language regularities than with experiential or intentional processes. LLMs may serve as research and training tools for studying the linguistic aspects of mentalization.

Stefano Epifani, Giuliano Castigliego, Giuliano Razzicchia +2

Digital Transformation Institute, Rome, Italy · Department of Communication and Social Research, Sapienza University of Rome, Italy · Psychoanalytic Academy of Italian-Speaking Switzerland, Chur, Switzerland +1

Self-Reflection in Language Model Agents Theory of Mind Clinical Language Model Evaluation

Nov 1, 2025

MedRECT: A Bilingual Medical Reasoning Benchmark for Error Correction in Clinical Texts

Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains under-evaluated, particularly beyond English. We introduce MedRECT, a bilingual benchmark for Japanese and English that formulates medical error handling as three subtasks: error detection, error sentence extraction, and error correction. MedRECT-ja contains 663 samples derived from the Japanese Medical Licensing Examinations, while the separately sourced MedRECT-en contains 458 samples curated from MEDEC. We evaluate 11 LLMs across 17 configurations that cover proprietary and open-weight models, medical-domain specialization, and multiple reasoning settings. Qwen3-32B scores higher in its thinking mode than in its non-thinking mode on error detection F1 and sentence extraction accuracy in both subsets, with sentence extraction accuracy higher by 24.5 percentage points on MedRECT-ja and 10.3 on MedRECT-en. Several leading general-purpose reasoning models outperform all three evaluated medical-domain models on these two subtasks. Most models have lower point estimates on the Japanese subset, although absolute scores are not directly comparable because the subsets differ in source material and error distributions. LoRA fine-tuning yields higher sentence extraction accuracy and higher point estimates on all three reference-based correction similarity metrics in both languages. MedRECT provides an open, reusable evaluation resource for studying medical error correction and reasoning across Japanese and English. Our dataset and code are available at https://github.com/pfnet-research/medrect.

Naoto Iwase, Hiroki Okuyama, Junichiro Iwasawa

Preferred Networks, Inc., Tokyo, Japan · School of Medicine, Nagoya University, Nagoya, Japan

Multilingual Language Model Evaluation Healthcare Clinical Language Model Evaluation Clinical NLP Medical LLMs

Oct 20, 2025

Zoom In Disparities in Healthcare LLM Q&A

Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the reliability and consistency of multilingual Large Language Models (LLMs). We systematically examine cross-lingual disparities in pre-training source and factuality alignment in LLM answers for multilingual healthcare Q&A across English, German, Turkish, Chinese (Mandarin), and Italian. We (i) constructed Multilingual Wiki Health Care (MultiWikiHealthCare), a multilingual dataset from Wikipedia; (ii) analyzed cross-lingual healthcare coverage; (iii) assessed LLM response alignment with these references; and (iv) conducted a case study on factual alignment through the use of contextual information and Retrieval-Augmented Generation (RAG). Our findings reveal substantial cross-lingual disparities in both Wikipedia coverage and LLM factual alignment. Across LLMs, responses align more with English Wikipedia, even when the prompts are non-English. Providing contextual excerpts from non-English Wikipedia at inference time effectively shifts factual alignment toward culturally relevant knowledge. These results highlight practical pathways for building more equitable, multilingual AI systems for healthcare.

Ipek Baris Schlicht, Burcu Sayin, Zhixue Zhao +5

Universitat Politècnica de València, Valencia, Spain · University of Trento, Trento, Italy · University of Sheffield, Sheffield, UK +5

Multilingual Language Model Evaluation Knowledge Augmentation for Language Models Healthcare Multilingual QA Factual Knowledge in Language Models Clinical Language Model Evaluation

Aug 7, 2025

"Mirror" Large Language Model Evaluations of Depression are Criterion Contaminated

Large Language Model (LLM) studies that use language responses elicited from depression assessments to predict scores on those same assessments often report near-perfect prediction of depression. We refer to these as "Mirror" evaluations and demonstrate an applied case of criterion contamination. N = 110 participants completed both structured diagnostic depression interviews (Mirror condition) and life history interviews ("Non-Mirror" condition). LLMs were prompted to predict depression scores in each condition. As expected, Mirror evaluations were near-perfect. However, Non-Mirror evaluations also displayed prediction sizes considered outstanding in psychology. Further, both Mirror and Non-Mirror predictions correlated with Patient Health Questionnaire-9 scores at similar sizes, suggesting the Mirror condition's advantage collapses when predicting an independent depression measurement. Topic modeling revealed differing depression-related themes across interview types. Mirror evaluations are better considered as reliability evaluations than as validity evaluations. Incorporating Non-Mirror approaches in LLM depression assessment may support more valid and clinically-relevant applications. Keywords: large language models, psychological assessment, psychopathology, depression, reliability, validity, criterion contamination

Tong Li, Rasiq Hussain, Mehak Gupta +1

Division of Computational & Data Sciences (DCDS), Washington University in St. Louis, St. Louis, MO · Department of Computer Science, SMU · Department of Psychological & Brain Sciences, Washington University in St. Louis

Depression Detection Mental Health Benchmark Contamination Psychometric Validation Depression Severity Estimation Clinical Language Model Evaluation

Aug 5, 2025

Somatic in the East, Psychological in the West?: Investigating Clinically-Grounded Cross-Cultural Depression Symptom Expression in LLMs

Prior clinical psychology research shows that Western individuals with depression tend to report psychological symptoms, while Eastern individuals report somatic ones. We test whether Large Language Models (LLMs), which are increasingly used in mental health, reproduce these cultural patterns by prompting them with Western or Eastern personas. Results show that LLMs largely fail to replicate the patterns when prompted in English, though prompting in major Eastern languages (i.e., Chinese, Japanese, and Hindi) improves alignment in several configurations. Our analysis pinpoints two key reasons for this failure: the models' low sensitivity to cultural personas and a strong, culturally invariant symptom hierarchy that overrides cultural cues. These findings reveal that while prompt language is important, current general-purpose LLMs lack the robust, culture-aware capabilities essential for safe and effective mental health applications.

Shintaro Sakai, Jisun An, Migyeong Kang +1

Indiana University Bloomington, USA · Sungkyunkwan University, Republic of Korea

Mental Health Cultural Bias in Language Models Clinical Language Model Evaluation

Aug 1, 2025

Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications

The proliferation of Large Language Models (LLMs) in medicine has enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning, a cornerstone of clinical practice. This has catalyzed a shift from single-step answer generation to the development of LLMs explicitly designed for medical reasoning. This paper provides the first systematic review of this emerging field. We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies (e.g., supervised fine-tuning, reinforcement learning) and test-time mechanisms (e.g., prompt engineering, multi-agent systems). We analyze how these techniques are applied across different data modalities (text, image, code) and in key clinical applications such as diagnosis, education, and treatment planning. Furthermore, we survey the evolution of evaluation benchmarks from simple accuracy metrics to sophisticated assessments of reasoning quality and visual interpretability. Based on an analysis of 60 seminal studies from 2022-2025, we conclude by identifying critical challenges, including the faithfulness-plausibility gap and the need for native multimodal reasoning, and outlining future directions toward building efficient, robust, and sociotechnically responsible medical AI.

Zizhan Ma, Wenxuan Wang, Meidan Ding +7

The Chinese University of Hong Kong · Renmin University of China · Shenzhen University +3

LLM Prompting RL for Language Model Reasoning Multimodal Reasoning Clinical Language Model Evaluation Medical LLMs

Jul 31, 2025

PRiME: Pragmatic Reasoning In MEntal health

In this work, the task and domain interaction of reasoning and mental health are investigated. As large language models (LLMs) present exceptional text understanding and fluent text generation capability, we analyze whether they can reason in the high-stakes domain of mental health. We begin by introducing three datasets and three tasks leveraging the existing work in pragmatic reasoning. The analysis constitutes an evaluation of

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LLMs, including MentaLLaMA, which is a mental health reasoning and explanation generation model. The performance of the models remains on par with human evaluation, with the Mistral-7B model giving the best performance. We further analyze the performance of the models with error asymmetry ratio and conduct first-order attention rollout experiment with MentaLLaMA-7B. We present the results for the community reporting the architectural behaviour of the models for the input text and recommend further careful analysis for the high-stakes domain.

Sneha Oram, Tejomay Kishor Padole, Pushpak Bhattacharyya +1

Indian Institute of Technology, Bombay

Language Model Reasoning Evaluation Pragmatic Reasoning in Language Models Clinical Language Model Evaluation

Jul 29, 2025

HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs

Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy provides non-judgemental, evidence-based information intended to improve health outcomes and reduce associated safety risks. Some large language models (LLMs) have demonstrated a high level of medical reasoning, promising to address the information needs of people who use drugs (PWUD). However, their performance in relevant tasks remains largely unexplored. We introduce HarmReduction, a benchmark designed to evaluate LLMs' accuracy and safety risks in harm reduction information provision. The benchmark dataset (HR-Basic) has 2,160 question-answer-evidence pairs. The scope covers three tasks: checking safety boundaries, providing quantitative values, and inferring polysubstance use risks. We build the Instruction and RAG schemes to evaluate model behaviours based on their inherent knowledge and the integration of domain knowledge. Our results indicate that state-of-the-art LLMs still struggle to provide accurate harm reduction information, and sometimes, present severe safety risks to PWUD. This work contributes an evaluation framework for LLMs to deliver harm reduction information to avoid introducing negative health outcomes through the use of LLMs.

Kaixuan Wang, Chenxin Diao, Jason T. Jacques +2

University of Oxford, United Kingdom · University of Edinburgh, United Kingdom · University of St Andrews, United Kingdom +1

Healthcare Question Answering LLM Safety Evaluation Clinical Language Model Evaluation

Jun 28, 2025

Structured Prompting and Automated Evaluation in Fixed Synthetic Japanese-Language Counseling Dialogues

Large language models (LLMs) may support counseling training, yet evidence from Japanese-language interactions and automated quality ratings remains limited. We examined 18 fixed Japanese-language counseling transcripts generated through artificial intelligence (AI)-to-AI interactions under three counselor conditions: GPT-minimal (GPT-4-turbo with a minimal role instruction), GPT-SMDP (GPT-4-turbo with the Structured Multi-step Dialogue Prompt [SMDP]), and Claude-SMDP (Claude-3-Opus with SMDP). Fifteen counseling experts rated transcripts on four adapted global scales from the Motivational Interviewing Treatment Integrity coding manual and an overall-quality item; three newer LLMs independently rated the same transcripts in three iterations. In this fixed stimulus set, SMDP-condition dialogues received higher expert ratings for cultivating change talk, partnership, empathy, and overall quality than GPT-minimal dialogues; the two SMDP counselor models did not differ. LLM ratings were reproducible but generally more lenient than expert-reference ratings, particularly for softening sustain talk and overall quality. Simulated-client naturalness was below the scale midpoint. These findings provide an expert-referenced benchmark for Japanese-language AI counseling simulations and show that reproducible LLM ratings should not be treated as calibrated counseling-quality evidence without expert validation. This study does not test clinical effectiveness or human-client outcomes.

Keita Kiuchi, Yoshikazu Fujimoto, Hideyuki Goto +5

Japan National Institute of Occupational Safety and Health, Kanagawa, Japan · Kaze To Taiyo, Tokyo, Japan · Saga Occupational Health Association, Saga, Japan +5

Mental Health Counseling LLM-as-a-Judge Emotional Support Conversation LLM Prompting Clinical Language Model Evaluation

Feb 4, 2025

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription

In response to the success of proprietary Large Language Models (LLMs), there is a growing interest in developing open, non-proprietary LLMs and AI foundation models (AIFMs) for transparent use in academic, scientific, and non-commercial applications. Open models have substantial potential to improve healthcare applications. In this paper, we provide a comprehensive survey and a taxonomy of state-of-the-art open LLMs and AIFMs for clinical tasks. In addition, we empirically evaluate open-weight LLMs on a personalized-prescription task and compare them with a proprietary model with and without Retrieval-Augmented Generation (RAG). Across 100 synthetic patient profiles, the open-weight models matched or exceeded the proprietary baseline on overall suitability accuracy without retrieval augmentation (0.810--0.850 versus 0.780), although the confidence intervals overlap; with retrieval augmentation, every model improved, and the ranking closed to within 0.04. Gains from RAG were field-dependent rather than uniform. To highlight the clinical relevance of LLM-driven personalized prescriptions, we also conduct a subjective evaluation in which two raters, blinded to model and condition, scored responses independently of each other. Their ratings did not follow the accuracy gains: retrieval raised overall suitability accuracy for every model while lowering the two-rater means for two of the four on all five dimensions. Finally, we outline the ethical implications and potential risks associated with the misuse of advanced LLMs and AIFMs, highlighting the importance of responsible and transparent deployment in healthcare contexts. \textit{Data and code used in the paper will be released upon publication.

Mahdi Alkaeed, Sofiat Abioye, Adnan Qayyum +6

Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar · Birmingham City University, Birmingham, United Kingdom · College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Doha, Qatar +2

Medical LLMs Open-Weight Language Models Clinical Language Model Evaluation Clinical Decision Support

Sep 25, 2024

Building Multilingual Datasets for Predicting Mental Health Severity through LLMs: Prospects and Challenges

Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptation of widely-used mental health datasets, translated from English into six languages (e.g., Greek, Turkish, French, Portuguese, German, and Finnish). This dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages. By experimenting with GPT and Llama, we observe considerable variability in performance across languages, despite being evaluated on the same translated dataset. This inconsistency underscores the complexities inherent in multilingual mental health support, where language-specific nuances and mental health data coverage can affect the accuracy of the models. Through comprehensive error analysis, we emphasize the risks of relying exclusively on LLMs in medical settings (e.g., their potential to contribute to misdiagnoses). Moreover, our proposed approach offers significant cost savings for multilingual tasks, presenting a major advantage for broad-scale implementation.

Konstantinos Skianis, John Pavlopoulos, A. Seza Doğruöz

Department of Computer Science and Engineering, University of Ioannina, Greece · Department of Informatics, Athens University of Economics and Business, Greece · Archimedes/AthenaRC, Greece +1

Multilingual Language Model Evaluation Clinical Language Model Evaluation Depression Severity Estimation

Sep 3, 2024

Therapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions

Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human counselors in generating single-turn empathetic responses, fewer studies have compared their behaviors across multi-turn sessions. In this study, we compare the session-level behaviors of human peer counselors with those of an LLM, both trained on the same manual to deliver multi-turn, single-session Cognitive Behavioral Therapy (CBT). Our three-phase, mixed-methods study involved: (a) an 18-month ethnography of a peer support platform, where seven counselors iteratively refined CBT prompts through 110 self-counseling sessions and 60 weekly focus groups; (b) a novel session generation method that allows direct, controlled comparison of human and LLM counselors under matched conditions---client responses were drawn from publicly available human-led CBT sessions while counselor responses were generated by a CBT-prompted LLM; and (c) expert evaluations conducted by three licensed clinical psychologists. Through data triangulation, our results show a trade-off. Human peer counselors use relational techniques to interpret subtle cues, adapt CBT to users' values and cultural contexts, and use strategies such as small talk and contextually relevant self-disclosure to build rapport and guide the session, but often at the expense of session structure and therapeutic focus. LLM counselors, on the other hand, demonstrate greater methodological adherence to CBT techniques, but struggle to sustain turn-taking, frequently fail to distinguish between clinically important and trivial content, and are more prone to lecturing and imposing solutions. LLM counselors also tend to produce ``deceptive empathy'', excessively anthropomorphic responses that can inflate user expectations of genuine human care.

Zainab Iftikhar, Sean Ransom, Amy Xiao +2

Brown University, USA · Department of Psychiatry, Louisiana State University Health Sciences, USA · Alpert Medical School of Brown University, Brown University, USA

Mental Health Counseling Mental Health Conversational Agents Conversational Agents Clinical Language Model Evaluation Cognitive Behavioral Therapy