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EpistemeAI released Reasoning-Medical0.1-27B, a fine-tuned version of Qwen3.5-27B for medical reasoning, claiming to surpass MedGemma on several medical benchmarks by incorporating chain-of-thought reasoning on a curated dataset of 100,000 records.
This study evaluates nine ECG foundation models for Brugada syndrome detection, finding that pre-training provides optimization stability but not transferable clinical knowledge, challenging assumptions about the benefits of large-scale pre-training for rare diseases.
The author reflects that the hospital is a good place to think about the value of AI, but the practical application of AI in healthcare has not yet brought universal breakthroughs; for example, AI tools like Fable 5 and Doubao have limited effectiveness.
This paper introduces IRFE-ECG, a method for continual ECG deployment that separates expert retention from autonomous source inference using frozen features from ECGFounder, achieving strong performance without replaying raw ECGs.
This paper adapts a diffusion language model for interactive radiology report drafting, showing it matches autoregressive models in accuracy while offering unique infill capabilities that allow radiologists to fix report fragments and have the model fill in the text between them.
This paper presents a novel machine learning approach combining Sparse Random Projection and multinomial logistic regression for classifying central nervous system tumors from DNA methylation data, achieving state-of-the-art accuracy improvements of 4-5 percentage points over existing methods.
Introduces RareDxR1, an end-to-end reasoning-centric large language model for open-domain rare disease diagnosis from unstructured clinical notes, using a progressive training framework and reflection-enhanced reasoning sampling, achieving state-of-the-art accuracy.
CLExEval introduces a human-in-the-loop framework for evaluating LLM clinical reasoning under progressive information masking, revealing failure patterns such as verbosity bias, hidden knowledge paradox, and reasoning-to-output mismatch in models like GPT-4o-mini and HuatuoGPT-o1.
This paper proposes a Multi-View Gated Graph Attention Network for Alzheimer's Disease detection from spontaneous speech, using semantic, dependency, and co-occurrence graphs with an adaptive gated fusion mechanism. The model achieves 90.00% accuracy on the ADReSSo dataset, and the source code is publicly available.
This paper presents a blinded evaluation of clinical AI tools using real point-of-care queries from physicians, comparing specialized and general-purpose models across five dimensions. The specialized tool (OpenEvidence) outperformed general-purpose models on all axes, and the authors release the Real-POCQi benchmark.
MedEvoEval is a longitudinal evaluation framework for doctor agents that simulates outpatient episodes, assessing how agents acquire evidence, use resources, and evolve across episodes through memory and reflection mechanisms.
This paper demonstrates that large language models internally encode the strength of clinical evidence for claims, yet fail to accurately express this strength when asked, with stated evidence grades performing near chance.
NASA is testing Red Hat's RamaLama open source tool to run local LLM and VLM inference for a medical AI assistant on deep space missions, enabling autonomous real-time diagnostics without Earth communication.
The author uses Claude Code with Opus 4.8 to analyze an MRI scan, finding discrepancies with the initial diagnosis, and discusses the potential and limitations of AI in medical imaging interpretation.
This paper proposes the Clinical Harness, a runtime governance architecture for registering, orchestrating, guarding, and monitoring AI-enabled clinical capabilities, using osteoporosis as a demonstration case.
This paper presents RaDaR, a 32B open-source reasoning LLM trained on public and synthetic rare disease cases, which outperforms larger models like DeepSeek-R1 in diagnosis benchmarks and improves physician accuracy by 21.44 percentage points in a randomized trial.
MedBench v5 is a dynamic, process-oriented benchmark for clinical multimodal models that integrates hallucination detection and stress testing, moving beyond static QA to evaluate reasoning and stability under information-flow stressors.
This paper introduces REVEAL++, a differentiable phenotypic grouping method for vision-language contrastive learning, applied to retinal fundus images and clinical risk narratives for Alzheimer's disease risk prediction, outperforming discrete grouping baselines.
Researchers from Boston Children's Hospital, Harvard, and OpenAI used the OpenAI o3 Deep Research reasoning model to reanalyze 376 unsolved rare disease cases, leading to diagnoses in 18 additional cases (4.8% yield) after expert review and clinical confirmation. The study, published in NEJM AI, demonstrates how AI-assisted workflows can help experts revisit difficult cases as scientific knowledge evolves.
OpenAI highlights how o3 Deep Research can aid rare disease diagnosis by integrating clinical features, inheritance patterns, variant evidence, and scientific literature into actionable hypotheses for specialists.