2026· Annual Meeting of the Association for Computational Linguistics· pp. 43120-43132· 0 citations· 26 references
Computer Science
TL;DR
This work proposes MedQPA, an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report.
Abstract
Medical report generation from medical images is a vital AI task that helps doctors with diagnosis and marks a significant step toward creating general AI-powered medical systems. However, previous methods either fail to optimize factual accuracy or heavily depend on expert preference data. To overcome these challenges, we propose MedQPA, an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report. Additionally, we design MedQPA-Gen, a medical report generation pipeline that maximizes the MedQPA score through prompt engineering and reinforcement learning with MedQPA as a reward signal. We demonstrate that MedQPA is an accurate evaluation metric that closely correlates with human preferences. More importantly, MedQPA-Gen achieves higher human preference scores and better performance on downstream tasks. We open-source code at this repo https://github.com/MedQPA-gen/MedQPA-gen.
Medical Vision-Language Models (Med-VLMs) require reliable reasoning from fine-grained visual evidence, yet existing models can produce plausible clinical answers by relying on language priors or medical templates rather than truly attending to diagnosis-critical regions. On-Policy Distillation (OPD) offers dense token-level supervision on student-generated trajectories and provides a privacy-compatible means of capability transfer without requiring the redistribution of raw patient data. However, standard OPD uniformly distills all tokens, causing sparse evidence-dependent tokens to be diluted by abundant clinical narrative tokens. Inspired by the success of OPD in the large language model community, we propose \textbf{Med-OPD}, to our knowledge the first unified post-training framework that integrates on-policy distillation with medical evidence-aware supervision for Med-VLMs. We introduce \textbf{Medical Evidence Advantage} (MEA), a teacher-grounded counterfactual signal that uses an answer-aware hint to focus teacher scoring on evidence supporting the target diagnosis, and measures each token's dependence on medical visual evidence by comparing teacher likelihoods under the original and evidence-degraded imaging modalities. Based on MEA, Med-OPD redistributes the distillation signal at both the token and trajectory levels, emphasizing diagnosis-critical tokens and evidence-reliant rollouts. Experiments on OmniMedVQA subsets show that Med-OPD consistently outperforms SFT and standard OPD across CT, MRI, Disease Diagnosis, and Lesion Grading. These results demonstrate that evidence-aware distillation can better strengthen medical VLMs'reliance on key visual evidence and improve reliable multimodal medical reasoning. The source code and data is publicly available at: https://github.com/yunhang8658/MedOPD.git
Yunhang Qian, Jiaquan Yu, Jiawei Liu et al.· 1 citation
A novel multimodal RAG framework tailored for MedVQA is proposed, which leverages multimodal data, including medical images, reports, and generated captions, to provide more accurate clinical answers, and introduces a training paradigm that uses captions as auxiliary supervision, enhancing cross-modal alignment via contrastive learning.
Mai A. Shaaban, M. Zarei, Adnan Khan et al.· 0 citations
Developing artificial intelligence capable of clinical language comprehension and reliable diagnostic reasoning has remained a core challenge in biomedical engineering. While Large Language Models (LLMs) demonstrate significant potential in general natural language processing tasks, their direct application in the medical domain is severely constrained by parametric hallucinations and data silos. This paper introduces an end-to-end, resource-efficient, multilingual speech-driven Question-Answering (QA) framework optimized for localized clinical support. To accommodate deployment on consumer-grade execution environments, we implement Parameter-Efficient Fine-Tuning (PEFT) using Low-Rank Adaptation (LoRA) and 4-bit Quantized LoRA (QLoRA) configurations across open-source 3B and 7B parameter architectures. Human preference alignment is enforced via a stateful Reinforcement Learning with Human Feedback (RLHF) loop applying Proximal Policy Optimization (PPO). Crucially, to mitigate the vulnerabilities of passive information retrieval, we introduce an Active Validation Loop powered by Corrective Retrieval-Augmented Generation (CRAG). This validation engine is decoupled from the model harness using the Model Context Protocol (MCP), standardizing asynchronous lookups across dense vector repositories, clinical guidelines, and real-time electronic health registries.
Misha Patel· International Journal of Sci...· 0 citations
Uncommon and off-guideline cases are difficult for clinical decision support, because physicians must make a series of management decisions under diagnostic uncertainty and rarely see the full case at once. Most large language model (LLM) benchmarks for medicine score only the final diagnosis, yet much of clinical care turns on the next appropriate action: the next test to order, the imaging study to obtain, the specialist to involve, or the differential to pursue. We introduce MedUPSQA, a dataset of 21,874 mid-stream clinical decision points built from 5,535 real case reports, and MedUPS, an alignment framework that supervises models on these intermediate decisions as they unfold along a patient's trajectory. We segment free-text case presentations into chronologically ordered, accumulating clinical chunks and align models to predict the next step with reinforcement learning (GRPO), using an external LLM-as-a-Judge reward. This objective mirrors how clinicians actually meet patients, reasoning forward from accumulating evidence toward the next decision, rather than committing to a final label. Across three backbones, mid-stream alignment raises next-step accuracy from 55.2 to 66.7 for Qwen3.6-27B, from 47.2 to 57.8 for Qwen3.5-9B, and from 37.8 to 44.4 for HuatuoGPT-3-8B, with 95% CI. In several model scales we test the objective improves accuracy more than scale, with smaller models surpassing larger, frontier models we evaluate. We further train supervised fine-tuning (SFT) baselines on the mid-stream task, SFT improves all backbones above base, indicating the target framwork carries signal independently of the optimizer. We release the dataset, code, and aligned checkpoints.
Ofir Ben Shoham, O. Perets, Nir Grinberg et al.· 0 citations
In medical multiple-choice question answering (MCQA), Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of language models (LMs). However, since vanilla RAG indiscriminately utilizes retrieved documents, it can degrade LM performance. To address this, we propose MedJudgeRAG. Our framework represents retrieved documents as a dynamic knowledge graph (KG) composed of entities and relations. For each option, the model judges an evidence verdict from the retrieved documents and the KG. Based on the verdict combination, the model determines a knowledge utilization strategy to reason toward the final answer. These capabilities are trained via supervised fine-tuning using structured reasoning traces generated by a teacher LM. The training employs a weighted cross-entropy loss that differentially weights the KG and reasoning segments. Experiments on two medical MCQA benchmarks demonstrate that MedJudgeRAG consistently outperforms both vanilla RAG and parametric baselines. Furthermore, ablation analysis reveals that the dynamic KG is more effective as graph-conditioned supervision at training time than as an explicit output at inference time. Our code is available at https://github.com/hyu-amllab/medjudgerag, and the generated reasoning traces are released at https://huggingface.co/datasets/youarethewon/medjudgerag.
Seongwon Seo, Seung-Hwan Cho, Young-Min Kim· 0 citations
The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus on textual reasoning or isolated visual question-answering (VQA) tasks, lacking holistic integration of clinical narratives and medical imaging, and thus failing to assess the multimodal diagnostic synthesis capability central to expert clinical judgment. To bridge this gap, we introduce MedReaMM, a benchmark specifically designed to evaluate models'ability to synthesize heterogeneous clinical evidence consisting of detailed patient histories alongside multiple medical images into accurate differential diagnoses under a complete-information paradigm. Constructed from case reports sourced from top-tier medical journals and curated clinical case databases, MedReaMM comprises 625 expert-validated cases with an average of 2.79 medical images per case and a total of 1,042 standardized diagnoses annotated with ICD-11 codes. These cases predominantly represent rare, atypical, or multi-system presentations that demand expert-level evidence integration beyond routine pattern recognition. We evaluate 23 Large Multimodal Models (LMMs) and find that most achieve diagnostic accuracy scores below 50%, underscoring a substantial gap in multimodal diagnostic synthesis capability. Further analysis reveals that medical knowledge proficiency, medical image understanding, and evidence integration are all highly correlated with diagnostic performance.
Lai Wei, Yuchao Chen, Zhenbiao Cao et al.· 0 citations