Language models achieve strong performance in medical question answering, yet hallucinations persist even in domain-specialized models, particularly on complex clinical vignettes involving rare presentations, multi-hop reasoning, and ambiguous evidence requiring explicit relational grounding. We introduce OntoDiagnose, a training-free, ontology-grounded, clinician-inspired reasoning pipeline that augments parametric knowledge with structured evidence from SNOMED CT. Given a clinical question, OntoDiagnose extracts salient medical entities, grounds them in the ontology, explores a bounded subgraph, and compresses relevant relations into compact, interpretable evidence chains that guide reasoning. We evaluate OntoDiagnose across USMLE-style and biomedical QA benchmarks using MedGemma, BioMistral, MedPhi, and OpenBioLLM under zero-shot, few-shot, and chain-of-thought settings. OntoDiagnose consistently improves over prompt-only baselines, with an average gain of 9.6 accuracy points on MedQA and the largest relative gains for the lowest-scoring models. These results show the benefit of incorporating ontology-grounded evidence into medical question answering without modifying model parameters.

Vision-language pretraining has driven progress in medical image representation learning, but it depends on paired image-text data and can inherit reporting bias from clinical narratives. We study whether language-free predictive pretraining can produce an image encoder that transfers effectively to radiology report generation. RadJEPA is a chest-X-ray adaptation of I-JEPA, pretrained on approximately 840K unlabeled radiographs using latent context-to-target prediction. Our primary contribution is an extensive empirical evaluation of this language-free encoder for report generation: the frozen image encoder is coupled to a trainable two-layer projector and language decoder, and is also substituted into four established vision-language backbones. Across MIMIC-CXR and IU-Xray, RadJEPA matches or exceeds the evaluated image-only and image-text baselines on lexical, entity-relation, and clinical-label metrics. Controlled MIMIC-only comparisons provide evidence that the predictive objective contributes beyond domain-specific pretraining, while broader comparisons also reflect differences in pretraining data, model capacity, and input resolution. Complementary classification and segmentation experiments assess transfer beyond report generation.

In modern healthcare, radiology plays a pivotal role in diagnosing and managing diseases. However, the complexity of medical imaging data and the variability in interpretation can lead to inconsistencies and a lack of patient-centered insight in radiology reports. To address this challenge, a novel multimodal prompt-driven report generation framework Rad-Flamingo was developed, that integrates diverse data modalities—such as medical images, and clinical notes—to produce comprehensive and context-aware radiology reports. Our framework leverages innovative prompt engineering techniques to guide vision-language models in generating relevant information, ensuring these generated reports are not only accurate but also understandable to individual patients. A key feature of our framework is its ability to provide patient-centric explanations, offering clear and personalized insights into diagnostic findings and their implications. Additionally, we also demonstrate a synthetic data generation pipeline, to append any existing benchmark datasets’ findings and impressions with patient-centric explanation. Experimental results demonstrate that this framework’s effectiveness in enhancing report quality, improving understandability, and could foster better patient-doctor communication. This approach represents a significant step towards human-centered medical AI systems.

Multi-agent debate among large language models (LLMs) is widely assumed to improve reasoning by letting models correct one another. We audit that assumption for English medical question answering (QA). Five open-weight 4 to 8B medical and generalist LLMs answer 8,402 questions across six benchmarks as a council, measured against the same five prompted individually and a single larger Qwen3-VL-32B, text-only. The council beats every member on five of six datasets, but most of that accuracy is in place before debate runs: 67% of verdicts settle at Round 0, and on five of six those are the more accurate questions. Where debate runs, opinion change concentrates on one anchor; on the hardest benchmark 85% of moves toward it happen while it is wrong. There, agreement is worth less than disagreement: questions all five agree on are correct 6.6% of the time against 14.6% otherwise, tracking a shared bias toward the first option. The council also trails the larger model on four of six by up to 16.2 points, at seven times the model calls for 1.6 times the compute. Agreement among models is a measurement that can be biased, not evidence of correctness.

We introduce MedBench, a benchmark for evaluating medical language models as deliberating agents rather than isolated predictors. MedBench evaluates eight models (4B?32B) on 19,625 questions from six medical QA datasets using Consensus-Aware Model Panel (CAMP), a two-tier protocol in which five 4B?8B models answer independently, revise after observing peer reasoning, and escalate persistent disagreements to larger 20B?32B models. Compared with zero-shot, few-shot, and chain-of-thought baselines, CAMP shows that deliberation is not uniformly accuracy-improving, but reveals interaction-driven behaviors hidden by single-model evaluation. On PubMedQA without external context, the 4B?8B panel outperforms the evaluated 20B?32B individual zero-shot models (54.1% vs. 33.9%), and achieves the best evaluated result with context (75.7%), suggesting that structured interaction can sometimes complement scale. Across five datasets, initial inter-model agreement is positively associated with correctness and serves as a useful difficulty signal. However, on MedXpertQA, unanimous agreement yields only 6.6% accuracy despite 14.4% overall accuracy, suggesting correlated ignorance, where shared biases make consensus misleading. Error analysis shows that most failures are debate-insufficient cases, where incorrect majorities persist despite interaction (93?97%), while debate-harmful cases account for 3?7%. MedBench positions deliberative evaluation as a complement to accuracy-centric benchmarking, measuring when model interaction corrects errors, reinforces shared mistakes, or signals the need for stronger evidence and human review.

In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios, along with clinician-approved reasoned answers. Additionally, we implement a prompt driven by Chain of Thought (CoT) reasoning, CLINICR, to mirror the prospective process of incremental reasoning, reaching a correct response to medical questions. We empirically demonstrate how CLINICR outperforms the state-of-the-art 5-shot CoT-based prompt (Liévin et al., 2022). We also present an approach that mirrors real-life clinical practice by first exploring multiple differential diagnoses through MCQ-CLINICR and subsequently narrowing down to a final diagnosis using MCQ-ELIMINATIVE. Finally, emphasizing the importance of response verification in medical settings, we utilize a reward model mechanism, replacing the elimination process performed by MCQ-ELIMINATIVE.

Clinical practice frequently uses medical imaging for diagnosis and treatment. A significant challenge for automatic radiology report generation is that the radiology reports are long narratives consisting of multiple sentences for both abnormal and normal findings. Therefore, applying conventional image captioning approaches to generate the whole report proves to be insufficient, as these are designed to briefly describe images with short sentences. We propose a template-based approach to generate radiology reports from radiographs. Our approach involves the following: i) using a multilabel image classifier, produce the tags for the input radiograph; ii) using a transformer-based model, generate pathological descriptions (a description of abnormal findings seen on radiographs) from the tags generated in step (i); iii) using a BERT-based multi-label text classifier, find the spans in the normal report template to replace with the generated pathological descriptions; and iv) using a rule-based system, replace the identified span with the generated pathological description. We performed experiments with the two most popular radiology report datasets, IU Chest X-ray and MIMIC-CXR and demonstrated that the BLEU-1, ROUGE-L, METEOR, and CIDEr scores are better than the State-of-the-Art models by 25%, 36%, 44% and 48% respectively, on the IU X-RAY dataset. To the best of our knowledge, this is the first attempt to generate chest X-ray radiology reports by first creating small sentences for abnormal findings and then replacing them in the normal report template.