MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings
The paper introduces a pipeline for converting unstructured clinical text into HL7 FHIR R4 bundles, enabling evaluation of LLMs in realistic electronic health record settings. Applied to the MedCaseReasoning dataset, it produces MedCase-Structured, a synthetic benchmark achieving valid FHIR generation for 82.5% of cases. Key finding: LLMs show consistently lower diagnostic accuracy on structured FHIR inputs compared to plain text, underscoring the gap between standard benchmarks and real-world clinical deployment conditions.
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OpenMedReason: Large-scale multimodal medical reasoning corpus with 450K instances for clinical VLM training
Researchers introduce OpenMedReason, a 450K-instance open multimodal medical reasoning corpus with reasoning traces derived from human-authored biomedical literature rather than synthetic chains of thought. The dataset covers diverse medical imaging modalities and is paired with OpenMedReason-Bench, a held-out benchmark evaluating LVLMs on perception, medical knowledge, and rationale axes. Training with OpenMedReason yields a 20% average VQA accuracy improvement over base models and achieves performance within 4.2% of leading comparable-scale medical VLMs. Both the dataset and code are publicly released.
ClinEnv: Interactive Multi-Stage Long-Horizon EHR Benchmark for Clinical Agent Evaluation
ClinEnv is a new interactive benchmark that evaluates LLMs as attending physicians over real inpatient admissions using a Longitudinal Inpatient Simulation paradigm. Each case is decomposed into sequential decision stages where models must query four specialized agents before committing to medications, procedures, and diagnoses. Across seven evaluated models, the best achieves only 0.31 decision F1, with a sharp gap between diagnosis recovery (0.51 F1) and management actions (0.17 F1). The benchmark uniquely measures information-acquisition process quality alongside outcome quality, exposing a gap invisible to static or outcome-only evaluations.
MedRLM: Recursive multimodal agent framework for long-context clinical decision support
MedRLM is a proposed framework for clinical decision support that uses recursive multi-agent reasoning over heterogeneous patient data including EHRs, medical images, physiological sensor streams, and clinical guidelines. Rather than single-step prompting, it decomposes patient cases into an inspectable external environment coordinated by specialized agents, with a Clinical Evidence Graph Memory and sensor-triggered deeper reasoning. The paper outlines an evaluation design using public and credentialed clinical datasets spanning radiology, ECG, ICU time series, and referral outcomes. The work targets a gap between static medical QA benchmarks and real-world longitudinal clinical workflows.
Sentence-Level Clinical Provenance Categorization for Multidisciplinary Hospital Summarization Using Fine-Tuned Llama-3
This pilot study presents a pipeline for categorizing sentence-level clinical provenance across multi-source hospital notes, targeting structured summarization in high-complexity settings like the NICU. The authors fine-tune Llama-3 8B and 70B models on MedSecId (MIMIC-III annotations), achieving Macro F1 above 92% in-domain. Cross-domain evaluation reveals a scale-dependent transfer effect: SFT substantially improves the 70B model (+7% Macro F1) but yields only marginal gains for the 8B model. A quantized fine-tuned 70B model outperforms its full-precision baseline while reducing compute, suggesting quantized adaptation is viable for structured clinical NLP tasks.
ChronoMedKG: Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning
ChronoMedKG is a new biomedical knowledge graph containing 460,497 evidence-linked triples across 13,431 diseases, each annotated with temporal components such as onset window and progression stage. It is constructed via a multi-agent pipeline using multiple frontier LLMs extracting from PubMed/PMC, with multi-model consensus and credibility filtering. The accompanying ChronoTQA benchmark (3,341 questions) reveals frontier LLMs lose ~30 points on temporal vs. static clinical questions, while ChronoMedKG-based retrieval recovers 47–65% of long-tail failures compared to 17–29% for HPOA-RAG. The work addresses a significant gap in existing KGs (PrimeKG, Hetionet, iKraph) that treat disease associations as static facts.
MedMisBench: LLMs show fragile epistemic resilience under misleading medical context
Researchers introduce MedMisBench, a benchmark of 10,932 medical questions paired with 48,889 misleading context injections, to measure whether LLMs maintain correct medical judgment under adversarial pressure. Across 11 model configurations, mean accuracy drops from 71.1% to 38.0% when misleading context is injected, with authority-framed falsehoods achieving 69.5% attack success. A 14-member international clinical panel flagged serious potential harm in 38.2% of reviewed cases. The work argues that existing medical benchmarks measure knowledge but not robustness to manipulation, exposing a structural gap in LLM safety evaluation for healthcare.
ClinHallu benchmark diagnoses stage-wise hallucinations in medical multimodal LLM reasoning
Researchers from Alibaba DAMO Academy introduce ClinHallu, a benchmark of 7,031 validated instances designed to identify where hallucinations originate within medical MLLM reasoning pipelines. Each instance is annotated with a structured reasoning trace decomposed into Visual Recognition, Knowledge Recall, and Reasoning Integration stages, with stage-replacement interventions to measure the causal impact of correcting each stage. The paper also demonstrates that trace-supervised fine-tuning reduces stage-wise hallucinations, offering both diagnostic and mitigation value for clinical AI systems.
Systematic evaluation of LLM prompt sensitivity in healthcare settings reveals safety risks
Researchers conduct a sensitivity analysis of both general-purpose and medical-specific LLMs using the MedMCQA benchmark, testing robustness to lexical and syntactic prompt perturbations. The study finds that even minor phrasing changes can alter clinical advice, and adversarial prompts can produce dangerous outputs such as incorrect dosages or omitted critical findings. Both general-purpose models (GPT-3.5, Llama 3) and domain-specific models (ClinicalBERT, BioLlama3, BioBERT) exhibit this fragility, with syntactic reordering and misleading contextual cues proving more destabilizing than simple paraphrasing.