4arXiv cs.CL (Computation and Language)·
ClinPRISM: Cost-effective multimodal LLM framework for clinical time series question answering
Researchers introduce ClinPRISM, a multimodal LLM reasoning framework designed for question answering over irregular clinical time series (ICTS). The system combines an irregularity-aware multi-scale encoder, a temporal evidence distiller, and a progressive alignment strategy to handle the sparsity and asynchrony typical of clinical observations. Built on a 4B-parameter LLM backbone, it achieves state-of-the-art results on a held-out clinical benchmark using only 16 time-series tokens with 0.15-second inference latency. The authors also release a training corpus of 30,000 clinical time series with multi-scale descriptions and 41,000 instruction-tuning instances across 11 tasks.
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ClinMM-Bench: Multi-turn multimodal benchmark for clinical diagnostic reasoning across 1,089 real-world cases
Researchers introduce ClinMM-Bench, a benchmark of 1,089 real-world clinical cases and 3,760 medical images spanning eight specialties, designed to evaluate multimodal LLMs on multi-turn diagnostic reasoning with progressive information disclosure. Fifteen MLLMs were evaluated using a two-level framework assessing both diagnostic accuracy and reasoning quality. Proprietary models led on accuracy but all models showed limited rates of fully correct diagnoses, and error analysis identified five failure modes including visual hallucination and premature closure. The work addresses a gap in clinical AI evaluation by moving beyond single-turn isolated tasks toward more realistic diagnostic workflows.
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.
ClinFusion: Vision-centric multimodal LLM for holistic medical image understanding
Researchers introduce ClinFusion, a multimodal LLM system designed for clinical medical understanding, featuring a cascaded vision encoder architecture (Cascade Spatial-Aware Locality Fusion) that handles both 2D and native 3D medical images within a unified encoder. The system is evaluated on a new benchmark suite including MedIF-Bench and a region-of-interest-grounded report generation metric, claiming state-of-the-art results on 20 of 24 benchmarks against open-source medical MLLMs and outperforming GPT-5.2 and Gemini-3-Flash on 13 of 16 multimodal benchmarks. Blinded evaluation by board-certified radiologists confirms ClinFusion produces the highest-ranked radiology reports, and the proposed RoI-grounded metric shows the strongest correlation with expert judgment among automatic metrics tested.
PRISM-AH: Multimodal video framework for ambivalence and hesitancy recognition in health behaviour contexts
A new arXiv preprint introduces PRISM-AH, a multimodal video understanding framework designed to detect ambivalence and hesitancy (A/H) — conflicting affective states relevant to health behaviour change. The system fuses frozen vision, audio, and text encoders with a streaming model that scores cross-modal dissonance and uses a knowledge-guided LLM for late fusion reasoning. On a 525-video public test set, PRISM-AH achieves macro F1 of 0.6133 versus a zero-shot baseline of 0.2827. The work is a domain-specific application of multimodal reasoning to affective computing in a clinical/health context.
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.
Prism: Plug-in Infrastructure for Multimodal Continual Instruction Tuning Research
Prism is an open-source codebase designed to address engineering bottlenecks in Multimodal Continual Instruction Tuning (MCIT) research. It introduces a plugin registration mechanism that separates algorithmic development from backbone MLLM implementation, allowing new continual learning strategies to be integrated without modifying the underlying model codebase. This design aims to eliminate structural fragmentation across method-specific implementations and enable fair, reproducible comparisons at scale.
ClaMPAPP: Hybrid LLM-ML system uses language models as interfaces for pediatric appendicitis diagnosis
Researchers introduce ClaMPAPP, a hybrid clinical decision support system that uses an LLM solely for structured feature extraction from free-text clinical notes, then passes validated features to an XGBoost classifier for final diagnosis. Evaluated on two independent German pediatric appendicitis cohorts, ClaMPAPP outperformed end-to-end LLM baselines on diagnostic performance and showed greater robustness to narrative reordering. The work formalizes an 'LLM-as-interface, ML-as-predictor' design pattern that separates natural-language usability from predictive inference, offering a more auditable pathway for clinical AI.
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.