Clinical Language Model Evaluation

Ask about this topic

Overview

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

CardsList

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

Apr 9, 2026

IatroBench: A Pre-Registered Benchmark of Clinical Omission in Language Models

We introduce IatroBench, a benchmark with two axes of harm (commission and omission), comprising 60 pre-registered clinical scenarios, tested on 6 models. Matched scenarios are framed as a patient query and a doctor consultation, differing in register and request (with the implication of supervision by a treating physician in the latter). We analyse the responses of five different models and find that all share more information in the doctor framing than the patient framing (which we call "framing-contingent withholding"). For example, a model with strong safety training provides a benzodiazepine tapering schedule to a doctor, but does not provide this schedule to a patient who requests it. We use Claude Opus 4.6 for structured evaluation, and Gemini 3 Flash as our primary judge, to score model responses against a physician's rubrics. Our primary judge agrees with physicians' omission scores about as well as physicians agree with each other. We find a decoupling gap of +0.38 (p = 0.003) on average across models. With our primary judge (checked by physicians) the decoupling gap is +0.22 (95% CI 0.10-0.36, p = 0.0014). We find three distinct patterns underlying this gap, exemplified by each of the models below. In the doctor framing, Claude Opus demonstrates that it has the information, and withholds it in the patient framing. Llama 4 performs poorly in both framings, meaning the decoupling gap cannot distinguish between withholding and incompetence. Finally, GPT-5.2 (excluded from this analysis) failed to return text for 33.2% of doctor responses, compared to 0% of layperson responses. In 86.6% of cases that we score (through our structured evaluation) as having omission harms, our primary judge (Gemini 3 Flash) scores zero omission harm. Because our scenarios are designed to pit safety against helpfulness, these statistics hold only for this distribution.

David Gringras

Harvard T.H. Chan School of Public Health

LLM Safety BenchmarksHealthcareLLM Safety EvaluationClinical Language Model Evaluation
AskPDF
Apr 8, 2026

To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Models

BACKGROUND: Recent studies have shown that domain-adapted large language models (LLMs) do not consistently outperform general-purpose counterparts on standard medical benchmarks, raising questions about the need for specialized clinical adaptation. METHODS: We systematically compare general and clinical LLMs on a diverse set of multiple choice clinical question answering tasks in English and Spanish. We introduce a perturbation based evaluation benchmark that probes model robustness, instruction following, and sensitivity to adversarial variations. Our evaluation includes, one-step and two-step question transformations, multi prompt testing and instruction guided assessment. We analyze a range of state-of-the-art clinical models and their general-purpose counterparts, focusing on Llama 3.1-based models. Additionally, we introduce Marmoka, a family of lightweight 8B-parameter clinical LLMs for English and Spanish, developed via continual domain-adaptive pretraining on medical corpora and instructions. RESULTS: The experiments show that clinical LLMs do not consistently outperform their general purpose counterparts on English clinical tasks, even under the proposed perturbation based benchmark. However, for the Spanish subsets the proposed Marmoka models obtain better results compared to Llama. CONCLUSIONS: Our results show that, under current short-form MCQA benchmarks, clinical LLMs offer only marginal and unstable improvements over general-purpose models in English, suggesting that existing evaluation frameworks may be insufficient to capture genuine medical expertise. We further find that both general and clinical models exhibit substantial limitations in instruction following and strict output formatting. Finally, we demonstrate that robust medical LLMs can be successfully developed for low-resource languages such as Spanish, as evidenced by the Marmoka models.

Ane G. Domingo-Aldama, Iker De La Iglesia, Maitane Urruela +2

Medical LLMsDomain Adaptation for LLMsClinical Language Model EvaluationMultilingual Language Model EvaluationLanguage Model RobustnessInstruction Following
AskPDF
Apr 8, 2026

MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors

Interactive medical dialogue benchmarks have shown that LLM diagnostic accuracy degrades significantly when interacting with non-cooperative patients, yet existing approaches either apply adversarial behaviors without graded severity or case-specific grounding, or reduce patient non-cooperation to a single ungraded axis, and none analyze cross-dimension interactions. We introduce MedDialBench, a benchmark enabling controlled, dose-response characterization of how individual patient behavior dimensions affect LLM diagnostic robustness. It decomposes patient behavior into five dimensions -- Logic Consistency, Health Cognition, Expression Style, Disclosure, and Attitude -- each with graded severity levels and case-specific behavioral scripts. This controlled factorial design enables graded sensitivity analysis, dose-response profiling, and cross-dimension interaction detection. Evaluating five frontier LLMs across 7,225 dialogues (85 cases x 17 configurations x 5 models), we find a fundamental asymmetry: information pollution (fabricating symptoms) produces 1.7-3.4x larger accuracy drops than information deficit (withholding information), and fabricating is the only configuration achieving statistical significance across all five models (McNemar p < 0.05). Among six dimension combinations, fabricating is the sole driver of super-additive interaction: all three fabricating-involving pairs produce O/E ratios of 0.70-0.81 (35-44% of eligible cases fail under the combination despite succeeding under each dimension alone), while all non-fabricating pairs show purely additive effects (O/E ~ 1.0). Inquiry strategy moderates deficit but not pollution: exhaustive questioning recovers withheld information, but cannot compensate for fabricated inputs. Models exhibit distinct vulnerability profiles, with worst-case drops ranging from 38.8 to 54.1 percentage points.

Xiaotian Luo, Xun Jiang, Jiangcheng Wu

Shanda Group

Adversarial Robustness of Language ModelsMedical LLMsAdversarial RobustnessClinical Language Model EvaluationClinical Decision-Making
AskPDF
Apr 8, 2026

Multilingual Cognitive Impairment Detection in the Era of Foundation Models

We evaluate cognitive impairment (CI) classification from transcripts of speech in English, Slovene, and Korean. We compare zero-shot large language models (LLMs) used as direct classifiers under three input settings -- transcript-only, linguistic-features-only, and combined -- with supervised tabular approaches trained under a leave-one-out protocol. The tabular models operate on engineered linguistic features, transcript embeddings, and early or late fusion of both modalities. Across languages, zero-shot LLMs provide competitive no-training baselines, but supervised tabular models generally perform better, particularly when engineered linguistic features are included and combined with embeddings. Few-shot experiments focusing on embeddings indicate that the value of limited supervision is language-dependent, with some languages benefiting substantially from additional labelled examples while others remain constrained without richer feature representations. Overall, the results suggest that, in small-data CI detection, structured linguistic signals and simple fusion-based classifiers remain strong and reliable signals.

Damar Hoogland, Boshko Koloski, Jaya Caporusso +5

Jožef Stefan Institute, Ljubljana, Slovenia · Jožef Stefan International Postgraduate School, Ljubljana, Slovenia · Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria +2

Speech-Based Dementia DetectionClinical Language Model EvaluationText ClassificationFeature FusionMultilingual Language Model Evaluation
AskPDF
Apr 7, 2026

FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts

The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHabits Shared Task. Our team participated in subtask 1, which aims to detect substance use and abuse mentions in clinical case reports and classify them in four categories (Tobacco, Alcohol, Cannabis, and Drug). We explored various methods of utilizing LLMs for the task, including zero-shot, few-shot, and prompt optimization, and found that GPT-4.1's few-shot prompting performed the best in our experiments. Our method achieved an F1 score of 0.65 on the test set, demonstrating a promising result for recognizing named entities in languages other than English.

Sylvia Vassileva, Ivan Koychev, Svetla Boytcheva

Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria · Graphwise, Sofia, Bulgaria

Clinical Information ExtractionNamed Entity RecognitionClinical NLPLLM Information ExtractionClinical Language Model Evaluation
AskPDF
Apr 7, 2026

A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

Large language models (LLMs) show promise for extracting clinically meaningful information from unstructured health records, yet their translation into real-world settings is constrained by the lack of scalable and trustworthy validation approaches. Conventional evaluation methods rely heavily on annotation-intensive reference standards or incomplete structured data, limiting feasibility at population scale. We propose a multi-stage validation framework for LLM-based clinical information extraction that enables rigorous assessment under weak supervision. The framework integrates prompt calibration, rule-based plausibility filtering, semantic grounding assessment, targeted confirmatory evaluation using an independent higher-capacity judge LLM, selective expert review, and external predictive validity analysis to quantify uncertainty and characterize error modes without exhaustive manual annotation. We applied this framework to extraction of substance use disorder (SUD) diagnoses across 11 substance categories from 919,783 clinical notes. Rule-based filtering and semantic grounding removed 14.59% of LLM-positive extractions that were unsupported, irrelevant, or structurally implausible. For high-uncertainty cases, the judge LLM's assessments showed substantial agreement with subject matter expert review (Gwet's AC1=0.80). Using judge-evaluated outputs as references, the primary LLM achieved an F1 score of 0.80 under relaxed matching criteria. LLM-extracted SUD diagnoses also predicted subsequent engagement in SUD specialty care more accurately than structured-data baselines (AUC=0.80). These findings demonstrate that scalable, trustworthy deployment of LLM-based clinical information extraction is feasible without annotation-intensive evaluation.

Maria Mahbub, Gregory M. Dams, Josh Arnold +9

Oak Ridge National Laboratory, Oak Ridge, TN, USA · Program Evaluation and Resource Center, Office of Mental Health and Office of Suicide Prevention, Department of Veterans Affairs, Menlo Park, CA, USA · Vanderbilt University Medical Center, Nashville, TN, USA +4

Clinical Information ExtractionClinical Language Model EvaluationLLM ReliabilityAutomated EvaluationLLM Grounding
AskPDF
Apr 7, 2026

Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation

The increasing use of large language models in mental health applications calls for principled evaluation frameworks that assess alignment with psychotherapeutic best practices beyond surface-level fluency. While recent systems exhibit conversational competence, they lack structured mechanisms to evaluate adherence to core therapeutic principles. In this paper, we study the problem of evaluating AI-generated therapist-like responses for clinically grounded appropriateness and effectiveness. We assess each therapists utterance along six therapeutic principles: non-judgmental acceptance, warmth, respect for autonomy, active listening, reflective understanding, and situational appropriateness using a fine-grained ordinal scale. We introduce FAITH-M, a benchmark annotated with expert-assigned ordinal ratings, and propose CARE, a multi-stage evaluation framework that integrates intra-dialogue context, contrastive exemplar retrieval, and knowledge-distilled chain-of-thought reasoning. Experiments show that CARE achieves an F-1 score of 63.34 versus the strong baseline Qwen3 F-1 score of 38.56 which is a 64.26 improvement, which also serves as its backbone, indicating that gains arise from structured reasoning and contextual modeling rather than backbone capacity alone. Expert assessment and external dataset evaluations further demonstrate robustness under domain shift, while highlighting challenges in modelling implicit clinical nuance. Overall, CARE provides a clinically grounded framework for evaluating therapeutic fidelity in AI mental health systems.

Abdullah Mazhar, Het Riteshkumar Shah, Aseem Srivastava +2

IIIT Delhi · MBZUAI · Wysa

Clinical Language Model EvaluationLLM EvaluationConversational Agents
AskPDF
Apr 7, 2026

CareTransition-Audit: A Benchmark to Audit Discharge Summaries for Efficient Care Transitions

Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions. Despite its critical role in patient safety, auditing discharge summaries relies on manual review and does not scale. We propose an automated framework for auditing discharge summaries using large language models (LLMs). Our approach operationalizes the DISCHARGED framework into a checklist of 46 questions. Using 50 summaries from the MIMIC-IV database, with clinician ground-truth labels, we benchmark 11 LLMs. Model-assessed mean documentation completeness ranges from 54.9% to 74.2%, and the best-performing models achieve a Cohen's kappa values around 0.5 against clinician labels, indicating moderate agreement. All models struggle to identify ambiguous documentation (Unclear), highlighting a key gap in current automated auditing. This work provides a clinician-validated benchmark and zero-shot baselines for systematic quality improvement in clinical documentation.

Akshat Dasula, Prasanna Desikan, Jaideep Srivastava +2

Centific AI Research, Redmond, USA · Department of Computer Science & Engineering, University of Minnesota-Twin Cities, Minneapolis, USA

LLM AuditingClinical Language Model Evaluation
AskPDF
Apr 6, 2026

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives

Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language models (LLMs) offer a promising opportunity to use artificial intelligence to improve screening. This study systematically explored both encoder- and decoder-based generative LLMs for screening clinical narratives to facilitate clinical trial recruitment. We examined both general-purpose LLMs and medical-adapted LLMs and explored three strategies to alleviate the "Lost in the Middle" issue when handling long documents, including 1) Original long-context: using the default context windows of LLMs, 2) NER-based extractive summarization: converting the long document into summarizations using named entity recognition, 3) RAG: dynamic evidence retrieval based on eligibility criteria. The 2018 N2C2 Track 1 benchmark dataset is used for evaluation. Our experimental results show that the MedGemma model with the RAG strategy achieved the best micro-F1 score of 89.05%, outperforming other models. Generative LLMs have remarkably improved trial criteria that require long-term reasoning across long documents, whereas trial criteria that span a short piece of context (e.g., lab tests) show incremental improvements. The real-world adoption of LLMs for trial recruitment must consider specific criteria for selecting among rule-based queries, encoder-based LLMs, and generative LLMs to maximize efficiency within reasonable computing costs.

Ziyi Chen, Mengxian Lyu, Cheng Peng +1

Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL

Medical LLMsClinical Language Model EvaluationRetrieval-Augmented GenerationLong-Context Language Model Evaluation
AskPDF
Apr 6, 2026

This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA

Patients are increasingly turning to large language models (LLMs) with medical questions that are complex and difficult to articulate clearly. However, LLMs are sensitive to prompt phrasings and can be influenced by the way questions are worded. Ideally, LLMs should respond consistently regardless of phrasing, particularly when grounded in the same underlying evidence. We investigate this through a systematic evaluation in a controlled retrieval-augmented generation (RAG) setting for medical question answering (QA), where expert-selected documents are used rather than retrieved automatically. We examine two dimensions of patient query variation: question framing (positive vs. negative) and language style (technical vs. plain language). We construct a dataset of 6,614 query pairs grounded in clinical trial abstracts and evaluate response consistency across eight LLMs. Our findings show that positively- and negatively-framed pairs are significantly more likely to produce contradictory conclusions than same-framing pairs. This framing effect is further amplified in multi-turn conversations, where sustained persuasion increases inconsistency. We find no significant interaction between framing and language style. Our results demonstrate that LLM responses in medical QA can be systematically influenced through query phrasing alone, even when grounded in the same evidence, highlighting the importance of phrasing robustness as an evaluation criterion for RAG-based systems in high-stakes settings.

Hye Sun Yun, Geetika Kapoor, Michael Mackert +4

Northeastern University · UC Berkeley · UT Austin +2

Framing Effects in Language ModelsMedical LLMsReasoning Consistency in Language ModelsClinical Language Model EvaluationRetrieval-Augmented Generation
AskPDF
Apr 6, 2026

Benchmarking Multi-turn Medical Diagnosis: Hold, Lure, and Self-Correction

Large language models (LLMs) achieve high accuracy in medical diagnosis when all clinical information is provided in a single turn, yet how they behave under multi-turn evidence accumulation closer to real clinical reasoning remains unexplored. We introduce MINT (Medical Incremental N-Turn Benchmark), a high-fidelity, multi-turn medical diagnosis benchmark comprising 1,035 cases with clinically labeled evidence shards, controlled turn granularity, and information-preserving decomposition. Through systematic evaluation of 11 LLMs on MINT, we uncover three persistent behavioral patterns that significantly impact diagnostic decisions: (1) intent to answer, models rush to answer before sufficient evidence has been observed, with over 55% of answers committed within the first two turns; (2) self-correction, incorrect-to-correct answer revisions occur at up to 10.6 times the rate of correct-to-incorrect flips, revealing a latent capacity for self-correction that premature commitment forecloses; and (3) strong lures, clinically salient information such as laboratory results trigger premature answering even when models are explicitly instructed to wait. We translate these findings into clinically actionable guidance: deferring the diagnostic question to later turns reduces premature answering and improves accuracy at the first point of commitment by up to 62.6%, while reserving salient clinical evidence for later turns prevents a catastrophic accuracy drop of up to 23.3% caused by premature commitment. Our work provides both a controlled evaluation framework and concrete recommendations for improving the reliability of LLMs in multi-turn medical diagnosis.

Jinrui Fang, Runhan Chen, Xu Yang +9

The University of Texas at Austin · New York University · The University of Texas Southwestern Medical Center +2

Clinical Language Model EvaluationMedical DiagnosisLLM ReliabilityClinical ReasoningLLM Self-Correction
AskPDF
Apr 5, 2026

MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction

Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful concepts are frequently implied rather than explicitly stated in medical narratives. Existing benchmarks with human-annotated evidence spans underscore the importance of grounding extracted concepts in medical text. However, they predominantly focus on explicitly stated concepts instead of implicit concepts. We present MedicalBench, a benchmark for medical concept extraction with evidence grounding that evaluates implicit medical reasoning. MedicalBench formulates medical concept extraction as a verification task over medical note-concept pairs, coupled with sentence-level evidence identification. Built from MIMIC-IV discharge summaries and human-verified ICD-10 codes, the dataset is curated through a multi-stage large language model (LLM) triage pipeline followed by medical annotation and expert review. It deliberately includes implicit positives, semantically confusable negatives, and cases where LLM judgments disagree with medical expert assessments. We define two complementary evaluation tasks: (1) medical concept extraction and (2) sentence-level evidence retrieval, enabling assessment of both correctness and interpretability. Benchmarking state-of-the-art LLMs reveals that performance remains modest, highlighting the difficulty of extracting implicitly expressed concepts. We further show that performance is largely invariant to note length, indicating that MedicalBench isolates reasoning difficulty rather than superficial confounders. MedicalBench provides the first systematic benchmark for implicit, evidence-grounded medical concept extraction, offering a foundation for developing medical language models that can both identify medically relevant concepts and justify their predictions in a transparent and medically faithful manner.

Zhichao Yang, Gregory D. Lyng, Sanjit Singh Batra +1

Optum AI

Clinical Information ExtractionClinical Language Model EvaluationLLM GroundingImplicit Reasoning in Language Models
AskPDF
Apr 3, 2026

A Scoping Review of LLM-as-a-Judge in Healthcare and the MedJUDGE Framework

As large language models (LLMs) increasingly generate and process clinical text, scalable evaluation has become critical. LLM-as-a-Judge (LaaJ), which uses LLMs to evaluate model outputs, offers a scalable alternative to costly expert review, but its healthcare adoption raises safety and bias concerns. We conducted a PRISMA-ScR scoping review of six databases (January 2020-January 2026), screening 11,727 studies and including 49. The landscape was dominated by evaluation and benchmarking applications (n=37, 75.5%), pointwise scoring (n=42, 85.7%), and GPT-family judges (n=36, 73.5%). Despite growing adoption, validation rigor was limited: among 36 studies with human involvement, the median number of expert validators was 3, while 13 (26.5%) used none. Risk of bias testing was absent in 36 studies (73.5%), only 1 (2.0%) examined demographic fairness, and none assessed temporal stability or patient context. Deployment remained limited, with 1 study (2.0%) reaching production and four (8.2%) prototype stage. Importantly, these gaps may interact: when judges and evaluated systems share training data or architectures, they may inherit similar blind spots, and agreement metrics may fail to distinguish true validity from shared errors. Minimal human oversight, limited bias assessment, and model monoculture together represent a governance gap where current validation may miss clinically significant errors. To address this, we propose MedJUDGE (Medical Judge Utility, De-biasing, Governance and Evaluation), a risk-stratified three-pillar framework organized around validity, safety, and accountability across clinical risk tiers, providing deployment-oriented evaluation guidance for healthcare LaaJ systems.

Chenyu Li, Zohaib Akhtar, Mingu Kwak +13

Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA · Department of Health Information Management, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA · OpenCura, Health Innovation Consortium +6

LLM-as-a-JudgeClinical Language Model EvaluationLanguage Model Safety EvaluationBias in LLM-as-a-Judge
AskPDF
Apr 2, 2026

Is Clinical Text Enough? A Multimodal Study on Mortality Prediction in Heart Failure Patients

Accurate short-term mortality prediction in heart failure (HF) remains challenging, particularly when relying on structured electronic health record (EHR) data alone. We evaluate transformer-based models on a French HF cohort, comparing text-only, structured-only, multimodal, and LLM-based approaches. Our results show that enriching clinical text with entity-level representations improves prediction over CLS embeddings alone, and that supervised multimodal fusion of text and structured variables achieves the best overall performance. In contrast, large language models perform inconsistently across modalities and decoding strategies, with text-only prompts outperforming structured or multimodal inputs. These findings highlight that entity-aware multimodal transformers offer the most reliable solution for short-term HF outcome prediction, while current LLM prompting remains limited for clinical decision support.

Oumaima El Khettari, Virgile Barthet, Guillaume Hocquet +3

Nantes Univ., École Centrale Nantes, CNRS, LS2N, UMR 6004, 44000 Nantes, France · Université Paris-Saclay, CNRS, LISN, Orsay, France · Hôpital Saint Joseph, DIMID, et Service de Cardiologie, Paris, France

Mortality PredictionMultimodal FusionClinical Language Model Evaluation
AskPDF
Mar 31, 2026

Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study

Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by eliciting explicit intermediate reasoning; meanwhile, self-reflective (self-corrective) prompting has been widely claimed to enhance model reliability by prompting LLMs to critique and revise their own reasoning, yet its effectiveness in safety-critical medical settings remains unclear. In this work, we conduct an exploratory analysis of self-reflective reasoning for medical multiple-choice question answering: using GPT-4o and GPT-4o-mini, we compare standard CoT prompting with an iterative self-reflection loop and track how predictions evolve across reflection steps on three widely used medical QA benchmarks (MedQA, HeadQA, and PubMedQA). We analyze whether self-reflection leads to error correction, error persistence, or the introduction of new errors. Our results show that self-reflective prompting does not consistently improve accuracy and its impact is highly dataset- and model-dependent: it yields modest gains on MedQA but provides limited or negative benefits on HeadQA and PubMedQA, and increasing the number of reflection steps does not guarantee better performance. These findings highlight a gap between reasoning transparency and reasoning correctness, suggesting that self-reflective reasoning is better viewed as an analytical tool for understanding model behavior rather than a standalone solution for improving medical QA reliability.

Zaifu Zhan, Mengyuan Cui, Rui Zhang

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, USA · Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, USA · Department of Software Engineering, University of St. Thomas, Saint Paul,USA

LLM Self-CorrectionLLM ReliabilityMedical QAClinical Language Model Evaluation
AskPDF
Mar 31, 2026

Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations

Large language models (LLMs) are increasingly used for medical consultation and health information support, where safety depends not only on medical knowledge but also on robust responses to unclear, inconsistent, or misleading patient input. However, most existing medical LLM evaluations assume idealized and well-posed patient questions, limiting their realism. We study challenging patient behaviors that commonly arise in real medical consultations and complicate safe clinical reasoning. We define four clinically grounded categories of such behaviors: information contradiction, factual inaccuracy, self-diagnosis, and care resistance. For each behavior, we specify concrete failure criteria that capture unsafe responses. Building on four existing medical dialogue datasets, we introduce CPB-Bench (Challenging Patient Behaviors Benchmark), a bilingual (English and Chinese) benchmark of multi-turn dialogues annotated for these behaviors. We find that although models perform well overall, they exhibit consistent behavior-specific failures, especially when handling contradictory or medically implausible patient information. We further evaluate four intervention strategies and find inconsistent improvements, with some interventions introducing unnecessary corrections.

Yahan Li, Xinyi Jie, Wanjia Ruan +5

University of Southern California

HealthcareClinical ReasoningLLM Safety EvaluationClinical Language Model EvaluationLanguage Model RobustnessMedical LLMs
AskPDF
Mar 31, 2026

AI-Generated Prior Authorization Letters: Strong Clinical Content, Weak Administrative Scaffolding

Prior authorization remains one of the most burdensome administrative processes in U.S. healthcare, consuming billions of dollars and thousands of physician hours each year. While large language models have shown promise across clinical text tasks, their ability to produce submission-ready prior authorization letters has received only limited attention, with existing work confined to single-case demonstrations rather than structured multi-scenario evaluation. We assessed three commercially available LLMs (GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Pro) across 45 physician-validated synthetic scenarios spanning rheumatology, psychiatry, oncology, cardiology, and orthopedics. All three models generated letters with strong clinical content: accurate diagnoses, well-structured medical necessity arguments, and thorough step therapy documentation. However, a secondary analysis of real-world administrative requirements revealed consistent gaps that clinical scoring alone did not capture, including absent billing codes, missing authorization duration requests, and inadequate follow-up plans. These findings reframe the question: the challenge for clinical deployment is not whether LLMs can write clinically adequate letters, but whether the systems built around them can supply the administrative precision that payer workflows require.

Moiz Sadiq Awan, Maryam Raza

Independent Researcher

Medical LLMsMedical Report GenerationClinical Language Model Evaluation
AskPDF
Mar 28, 2026

Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa

Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health care system. This paper presents an experimental study on the development of a domain-specific Large Language Model (DS-LLM) for TB care that can help to alleviate the burden on patients and healthcare providers. To achieve this, a literature review was conducted to understand current LLM development strategies, specifically in the medical domain. Thereafter, data were collected from South African TB guidelines, selected TB literature, and existing benchmark medical datasets. We performed LLM fine-tuning by using the Quantised Low-Rank Adaptation (QLoRA) algorithm on a medical LLM (BioMistral-7B), and also implemented Retrieval-Augmented Generation using GraphRAG. The developed DS-LLM was evaluated against the base BioMistral-7B model and a general-purpose LLM using a mix of automated metrics and quantitative ratings. The results show that the DS-LLM had better performance compared to the base model in terms of its contextual alignment (lexical, semantic, and knowledge) for TB care in South Africa.

Thokozile Khosa, Olawande Daramola

Department of Computer Science, University of Pretoria, South Africa · Department of Informatics, University of Pretoria, South Africa

Medical LLMsHealthcareLLM Fine-TuningClinical Language Model Evaluation
AskPDF
Mar 27, 2026

Clinical named entity recognition in the Portuguese language: a benchmark of modern BERT models and LLMs

Clinical notes contain valuable unstructured information. Named entity recognition (NER) enables the automatic extraction of medical concepts; however, benchmarks for Portuguese remain scarce. In this study, we aimed to evaluate BERT-based models and large language models (LLMs) for clinical NER in Portuguese and to test strategies for addressing multilabel imbalance. We compared BioBERTpt, BERTimbau, ModernBERT, and mmBERT with LLMs such as GPT-5 and Gemini-2.5, using the public SemClinBr corpus and a private breast cancer dataset. Models were trained under identical conditions and evaluated using precision, recall, and F1-score. Iterative stratification, weighted loss, and oversampling were explored to mitigate class imbalance. The mmBERT-base model achieved the best performance (micro F1 = 0.76), outperforming all other models. Iterative stratification improved class balance and overall performance. Multilingual BERT models, particularly mmBERT, perform strongly for Portuguese clinical NER and can run locally with limited computational resources. Balanced data-splitting strategies further enhance performance.

Vinicius Anjos de Almeida, Sandro Saorin da Silva, Josimar Chire +8

Spesia, Curitiba - PR, Brazil · Faculdade de Medicina, Universidade de São Paulo, São Paulo - SP, Brazil · Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba - PR, Brazil +2

Named Entity RecognitionClinical NLPClinical Language Model EvaluationMedical LLMs
AskPDF
Mar 27, 2026

Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models

Clinicians often need to retrieve patient-specific information from electronic health records (EHRs), a task that is time-consuming and error-prone. We present a locally deployable Clinical Contextual Question Answering (CCQA) framework that answers clinical questions directly from EHRs without external data transfer. Open-source large language models (LLMs) ranging from 4B to 70B parameters were benchmarked under fully offline conditions using 1,664 expert-annotated question-answer pairs derived from records of 183 patients. The dataset consisted predominantly of Finnish clinical text. In free-text generation, Llama-3.1-70B achieved 95.3% accuracy and 97.3% consistency across semantically equivalent question variants, while the smaller Qwen3-30B-A3B-2507 model achieved comparable performance. In a multiple-choice setting, models showed similar accuracy but variable calibration. Low-precision quantization (4-bit and 8-bit) preserved predictive performance while reducing GPU memory requirements and improving deployment feasibility. Clinical evaluation identified clinically significant errors in 2.9% of outputs, and semantically equivalent questions occasionally yielded discordant responses, including instances where one formulation was correct and the other contained a clinically significant error (0.96% of cases). These findings demonstrate that locally hosted open-source LLMs can accurately retrieve patient-specific information from EHRs using natural-language queries, while highlighting the need for validation and human oversight in clinical deployment.

Mikko Saukkoriipi, Nicole Hernandez, Jaakko Sahlsten +2

Department of Computer Science, Aalto University School of Science, Espoo, 02150, Finland. · Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland. · Department of Oncology, TAYS Cancer Centrer, Tampere University Hospital, Tampere, 33520, Finland.

EHR QAClinical QAMedical LLMsClinical Language Model Evaluation
AskPDF
Mar 26, 2026

When Chain-of-Thought Backfires: Evaluating Prompt Sensitivity in Medical Language Models

Large Language Models (LLMs) are increasingly deployed in medical settings, yet their sensitivity to prompt formatting remains poorly characterized. We evaluate MedGemma (4B and 27B parameters) on MedMCQA (4,183 questions) and PubMedQA (1,000 questions) across a broad suite of robustness tests. Our experiments reveal several concerning findings. Chain-of-Thought (CoT) prompting decreases accuracy by 5.7% compared to direct answering. Few-shot examples degrade performance by 11.9% while increasing position bias from 0.14 to 0.47. Shuffling answer options causes the model to change predictions 59.1% of the time, with accuracy dropping up to 27.4 percentage points. Front-truncating context to 50% causes accuracy to plummet below the no-context baseline, yet back-truncation preserves 97% of full-context accuracy. We further show that cloze scoring (selecting the highest log-probability option token) achieves 51.8% (4B) and 64.5% (27B), surpassing all prompting strategies and revealing that models "know" more than their generated text shows. Permutation voting recovers 4 percentage points over single-ordering inference. These results demonstrate that prompt engineering techniques validated on general-purpose models do not transfer to domain-specific medical LLMs, and that reliable alternatives exist.

Binesh Sadanandan, Vahid Behzadan

SAIL Lab, University of New Haven, West Haven, CT 06516, USA

Prompt SensitivityLLM PromptingLanguage Model RobustnessMedical LLMsClinical Language Model EvaluationMedical QA
AskPDF
Mar 26, 2026

Same Verdict, Different Reasons: LLM-as-a-Judge and Clinician Disagreement on Medical Chatbot Completeness

LLM-as-a-Judge frameworks are increasingly trusted to automate evaluation in place of human experts, yet their reliability in high-stakes medical contexts remains unproven. We stress-test this assumption for detecting incomplete patient-facing medical responses, evaluating three rubric granularities (General-Likert, Analytical-Rubric, Dynamic-Checklist) and three backbone models across two clinician-annotated datasets, including HealthBench, the largest publicly available benchmark for medical response evaluation. LLM Judges discriminate complete from incomplete responses at and slightly above near chance (AUC 0.490.49--0.660.66); at the threshold required to recall 90%90\% of incomplete responses, clinicians must still review the vast majority of the dataset, offering no triage utility. Even when model and clinician verdicts agree, they rarely cite the same explanation; and when they diverge, false positives stem from over-flagging non-essential gaps while false negatives reflect outright detection failures. These results reveal that LLM Judges and clinicians apply fundamentally different completeness standards; a finding that undermines their use as autonomous evaluators or triage filters in clinical settings.

Alexandra DeLucia, Heyuan Huang, Sonal Joshi +3

Data Science and AI Institute, Johns Hopkins University

LLM-as-a-JudgeLLM ReliabilityClinical Language Model Evaluation
AskPDF
Mar 26, 2026

Doctorina MedBench: A Dialogue-Based Benchmark and Evaluation Framework for Agent-Based Medical AI

We present Doctorina MedBench, an evaluation framework for agent-based medical AI based on the simulation of physician-patient interactions. Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which an AI system must collect medical history, analyze available synthetic attachments when present, formulate differential diagnoses, and provide diagnostic and management recommendations. System performance is evaluated across separate task-level domains, including diagnosis, differential diagnosis, treatment, safety-critical condition handling, and dialogue-step behavior; the broader D.O.T.S. framework is used as a supplementary summary for diagnosis, observations/investigations, treatment, and step count. The framework also supports testing and quality-monitoring workflows intended to identify changes in model behavior during development. It supports safety-oriented cases, category-based sampling of synthetic clinical scenarios, and regression-style comparisons across system versions. In the reported study, the analyzed paired complete-case cohort consisted of 254 physician-authored synthetic clinical cases retained from 261 attempted case identifiers. The evaluation metrics are intended for comparative research on interactive medical AI systems and for studying clinical reasoning workflows in synthetic dialogue settings. Our results suggest that simulated clinical dialogue can provide a complementary assessment setting to traditional examination-style benchmarks, while the reported findings do not establish independent clinical validity, clinical effectiveness, or readiness for real-world deployment.

Anna Kozlova, Stanislau Salavei, Pavel Satalkin +3

A.I. Doctor Medical Assist LTD, 13 Myrtiotissis Street, Aqua Mansions, Office 1, Germasogeia, 4041 Limassol, Cyprus

Medical AI AgentsAI Agent BenchmarksClinical Language Model EvaluationMulti-Turn Dialogue EvaluationAI Agent Safety BenchmarksClinical Reasoning
AskPDF
Mar 26, 2026

A Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations

Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs during conversations. To address this gap, we introduce CPGBench, an automated framework benchmarking the clinical guideline detection and adherence capabilities of LLMs in multi-turn conversations. We collect 3,418 CPG documents from 9 countries/regions and 2 international organizations published in the last decade spanning across 24 specialties. From these documents, we extract 32,155 clinical recommendations with corresponding publication institute, date, country, specialty, recommendation strength, evidence level, etc. One multi-turn conversation is generated for each recommendation accordingly to evaluate the detection and adherence capabilities of 8 leading LLMs. We find that the 71.1%-89.6% recommendations can be correctly detected, while only 3.6%-29.7% corresponding titles can be correctly referenced, revealing the gap between knowing the guideline contents and where they come from. The adherence rates range from 21.8% to 63.2% in different models, indicating a large gap between knowing the guidelines and being able to apply them. To confirm the validity of our automatic analysis, we further conduct a comprehensive human evaluation involving 56 clinicians from different specialties. To our knowledge, CPGBench is the first benchmark systematically revealing which clinical recommendations LLMs fail to detect or adhere to during conversations. Given that each clinical recommendation may affect a large population and that clinical applications are inherently safety critical, addressing these gaps is crucial for the safe and responsible deployment of LLMs in real world clinical practice.

Andong Tan, Shuyu Dai, Jinglu Wang +7

Media Computing Group, Microsoft Research Asia, Beijing, China · Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China · School of Computer Science, Peking University, Beijing, China +6

Clinical Language Model EvaluationMedical LLMsLLM Safety Benchmarks
AskPDF
Mar 26, 2026

OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs

Large Language Models (LLMs) have shown remarkable capabilities for complex tasks, yet adaptation in medical domain, specifically mental health, poses specific challenges. Mental health is a rising concern globally with LLMs having large potential to help address the same. We highlight three primary challenges for LLMs in mental health - lack of high quality interpretable and knowledge grounded training data; training paradigms restricted to core capabilities, and evaluation of multi turn dialogue settings. Addressing it, we present oMind framework which includes training and aligning LLM agents for diverse capabilities including conversations; high quality ~164k multi-task SFT dataset, as a result of our generation pipeline based on Structured Knowledge retrieval, LLM based pruning, and review actions. We also introduce oMind-Chat - a novel multi turn benchmark dataset with expert annotated turn level and conversation level rubrics. Our diverse experiments on both core capabilities and conversations shows oMind LLMs consistently outperform baselines. oMind-LLM also shows significantly better reasoning with up to 80% win rate.

Suraj Racha, Prashant Harish Joshi, Utkarsh Maurya +6

Indian Institute of Technology Bombay

Medical LLMsLLM Fine-TuningLLM GroundingClinical Language Model Evaluation
AskPDF
Mar 25, 2026

Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset

Clinical documentation is a critical factor for patient safety, diagnosis, and continuity of care. The administrative burden of EHRs is a significant factor in physician burnout. This is a critical issue for low-resource languages, including Finnish. This study aims to investigate the effectiveness of a domain-aligned natural language processing (NLP); large language model for medical transcription in Finnish by fine-tuning LLaMA 3.1-8B on a small validated corpus of simulated clinical conversations by students at Metropolia University of Applied Sciences. The fine-tuning process for medical transcription used a controlled preprocessing and optimization approach. The fine-tuning effectiveness was evaluated by sevenfold cross-validation. The evaluation metrics for fine-tuned LLaMA 3.1-8B were BLEU = 0.1214, ROUGE-L = 0.4982, and BERTScore F1 = 0.8230. The results showed a low n-gram overlap but a strong semantic similarity with reference transcripts. This study indicate that fine-tuning can be an effective approach for translation of medical discourse in spoken Finnish and support the feasibility of fine-tuning a privacy-oriented domain-specific large language model for clinical documentation in Finnish. Beside that provide directions for future work.

Mohammed Nowshad Ruhani Chowdhury, Mohammed Nowaz Rabbani Chowdhury, Sakari Lukkarinen

School of ICT and Industrial Management, Metropolia University of Applied Sciences, Helsinki, Finland · Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA

LLM Fine-TuningMedical LLMsClinical Language Model EvaluationLow-Resource Language Processing
AskPDF
Mar 20, 2026

Using LLM-as-a-Judge/Jury to Advance Scalable, Clinically-Validated Safety Evaluations of Model Responses to Users Demonstrating Psychosis

General-purpose Large Language Models (LLMs) are becoming widely adopted by people for mental health support. Yet emerging evidence suggests there are significant risks associated with high-frequency use, particularly for individuals suffering from psychosis, as LLMs may reinforce delusions and hallucinations. Existing evaluations of LLMs in mental health contexts are limited by a lack of clinical validation and scalability of assessment. To address these issues, this research focuses on psychosis as a critical condition for LLM safety evaluation by (1) developing and validating seven clinician-informed safety criteria, (2) constructing a human-consensus dataset, and (3) testing automated assessment using an LLM as an evaluator (LLM-as-a-Judge) or taking the majority vote of several LLM judges (LLM-as-a-Jury). Results indicate that LLM-as-a-Judge aligns closely with the human consensus (Cohen's κhuman×gemini=0.75κ_{\text{human} \times \text{gemini}} = 0.75, κhuman×qwen=0.68κ_{\text{human} \times \text{qwen}} = 0.68, κhuman×kimi=0.56κ_{\text{human} \times \text{kimi}} = 0.56) and that the best judge slightly outperforms LLM-as-a-Jury (Cohen's κhuman×jury=0.74κ_{\text{human} \times \text{jury}} = 0.74). Overall, these findings have promising implications for clinically grounded, scalable methods in LLM safety evaluations for mental health contexts.

May Lynn Reese, Markela Zeneli, Mindy Ng +3

Apart Research · Odyssean Institute · London School of Economics and Political Science +1

LLM SafetyLLM Safety EvaluationClinical Language Model EvaluationLLM-as-a-Judge
AskPDF
Mar 18, 2026

The Validity Gap in Health AI Evaluation: A Cross-Sectional Analysis of Benchmark Composition

Background: Clinical trials rely on transparent inclusion criteria to ensure generalizability. In contrast, benchmarks validating health-related large language models (LLMs) rarely characterize the "patient" or "query" populations they contain. Without defined composition, aggregate performance metrics may misrepresent model readiness for clinical use. Methods: We analyzed 18,707 consumer health queries across six public benchmarks using LLMs as automated coding instruments to apply a standardized 16-field taxonomy profiling context, topic, and intent. Results: We identified a structural "validity gap." While benchmarks have evolved from static retrieval to interactive dialogue, clinical composition remains misaligned with real-world needs. Although 42% of the corpus referenced objective data, this was polarized toward wellness-focused wearable signals (17.7%); complex diagnostic inputs remained rare, including laboratory values (5.2%), imaging (3.8%), and raw medical records (0.6%). Safety-critical scenarios were effectively absent: suicide/self-harm queries comprised <0.7% of the corpus and chronic disease management only 5.5%. Benchmarks also neglected vulnerable populations (pediatrics/older adults <11%) and global health needs. Conclusions: Evaluation benchmarks remain misaligned with real-world clinical needs, lacking raw clinical artifacts, adequate representation of vulnerable populations, and longitudinal chronic care scenarios. The field must adopt standardized query profiling--analogous to clinical trial reporting--to align evaluation with the full complexity of clinical practice.

Alvin Rajkomar, Pavan Sudarshan, Angela Lai +1

Apple

Benchmark ValidityClinical Language Model EvaluationMedical LLMs
AskPDF
Mar 17, 2026

Blending Human and LLM Expertise to Detect Hallucinations and Omissions in Mental Health Chatbot Responses

As LLM-powered chatbots are increasingly deployed in mental health services, detecting hallucinations and omissions has become critical for user safety. However, state-of-the-art LLM-as-a-judge methods often fail in high-risk healthcare contexts, where subtle errors can have serious consequences. We show that leading LLM judges achieve only 52% accuracy on mental health counseling data, with some hallucination detection approaches exhibiting near-zero recall. We identify the root cause as LLMs' inability to capture nuanced linguistic and therapeutic patterns recognized by domain experts. To address this, we propose a framework that integrates human expertise with LLMs to extract interpretable, domain-informed features across five analytical dimensions: logical consistency, entity verification, factual accuracy, linguistic uncertainty, and professional appropriateness. Experiments on a public mental health dataset and a new human-annotated dataset show that traditional machine learning models trained on these features achieve 0.717 F1 on our custom dataset and 0.849 F1 on a public benchmark for hallucination detection, with 0.59-0.64 F1 for omission detection across both datasets. Our results demonstrate that combining domain expertise with automated methods yields more reliable and transparent evaluation than black-box LLM judging in high-stakes mental health applications.

Khizar Hussain, Bradley A. Malin, Zhijun Yin +2

Virginia Tech, Blacksburg, VA, USA · Vanderbilt University Medical Center, Nashville, TN, USA

Language Model Error DetectionHallucination DetectionClinical Language Model Evaluation
AskPDF
Mar 17, 2026

Medical Reasoning with Large Language Models: A Survey and MR-Bench

Large language models (LLMs) have achieved strong performance on medical exam-style tasks, motivating growing interest in their deployment in real-world clinical settings. However, clinical decision-making is inherently safety-critical, context-dependent, and conducted under evolving evidence. In such situations, reliable LLM performance depends not on factual recall alone, but on robust medical reasoning. In this work, we present a comprehensive review of medical reasoning with LLMs. Grounded in cognitive theories of clinical reasoning, we conceptualize medical reasoning as an iterative process of abduction, deduction, and induction, and organize existing methods into seven major technical routes spanning training-based and training-free approaches. We further conduct a unified cross-benchmark evaluation of representative medical reasoning models under a consistent experimental setting, enabling a more systematic and comparable assessment of the empirical impact of existing methods. To better assess clinically grounded reasoning, we introduce MR-Bench, a benchmark derived from real-world hospital data. Evaluations on MR-Bench expose a pronounced gap between exam-level performance and accuracy on authentic clinical decision tasks. Overall, this survey provides a unified view of existing medical reasoning methods, benchmarks, and evaluation practices, and highlights key gaps between current model performance and the requirements of real-world clinical reasoning.

Xiaohan Ren, Chenxiao Fan, Wenyin Ma +4

School of Cyber Science and Technology, University of Science and Technology of China, Anhui, China · School of Artificial Intelligence and Data Science, University of Science and Technology of China, Anhui, China · Department of Infectious Disease, The First Affiliated Hospital of USTC, Anhui, China +2

Medical ReasoningClinical ReasoningClinical Language Model EvaluationClinical Decision-Making
AskPDF