medical

Presents FOCUS, a fine-tuning method that decouples expert personas in LLMs via orthogonal decomposition and an expert gating module, improving domain-specific task accuracy across financial, legal, and medical benchmarks.

Reasoning-Medical-27B is a fine-tuned Qwen3.6-27B model for advanced medical reasoning, trained on 370k Q&A examples with Chain-of-Thought reasoning using GRPO and Unsloth optimization.

MedLoCoMo is a new benchmark for evaluating LLMs on long-context, multi-session medical dialogue reasoning, constructed from MIMIC-IV data. It tests single-admission, cross-admission, and adversarial unanswerable questions, revealing that cross-admission reasoning remains challenging even for models with long context windows.

A couple paid over $800,000 for a gene-editing therapy for their daughter, who subsequently died.

This paper investigates cross-lingual clinical correctness drift in medical language models, finding that locally deployable models show significant safety degradation when queried in Hausa compared to English, while frontier models maintain competence, highlighting a critical gap in safety evaluation for low-resource settings.

The paper introduces ThReadMed-QA, a multi-turn medical dialogue dataset, and evaluates five LLMs on correcting patient misconceptions, finding substantial degradation over subsequent turns.

MedRealMM is a new multimodal benchmark for Chinese online medical consultation, built from real-world patient-doctor interactions, evaluating LLMs on next-response generation with clinical rubrics.

Damn muse 1.1 is claimed to outperform fable 5 in medical and legal use cases.

OpenMed 1.8 is an Apache-2.0 toolkit for clinical de-identification that runs entirely locally, with new support for Android, iOS, and browser platforms, and invites community contributions for version 1.9.

MedPMC is an automated framework that transforms medical literature into high-fidelity multimodal data for foundation models, achieving significant improvements across multiple benchmarks and clinical settings.

The paper introduces MedCalc-Pro, a new benchmark for evaluating LLMs in complex medical calculations involving single, multi, and nested calculator settings, along with an agent framework that improves performance through multi-tool selection and structured validation.

A tweet shares a Nature Medicine article, but the linked content is an error page due to browser issues.

FaithMed is a framework that trains LLMs for faithful evidence-based medical reasoning by integrating clinician-designed rubrics with reinforcement learning using step-level process reward assignment, achieving significant improvements over baselines on multiple medical benchmarks.

This paper introduces MedKGTab, a knowledge-injected framework that uses biomedical knowledge graphs to expand cross-domain features in tabular medical data, addressing data scarcity by generating high-fidelity biomedical profiles.

This paper adapts a mixture-of-experts diffusion language model, DiffusionGemma-26B, for interactive radiology report drafting, showing it matches or exceeds autoregressive models in medical VQA with 3.5-4.4x faster decoding and bidirectional infill capabilities.

IMCBench is a new benchmark for evaluating multimodal LLMs on image-grounded medical conversations, pairing clinical images with synthetic patient profiles. Evaluations across safety, accuracy, and uncertainty show that even strong models like Claude Opus 4.6 have safety issues, highlighting the need for multi-dimensional evaluation.

This paper proposes TriageRA-CCF, a method for adaptive rank budgeting in LoRA for medical question answering. It uses source-side signals (base-model confidence, clinical coverage, counterfactual proxy) to dynamically choose rank budgets, achieving modest accuracy gains on Qwen3-8B and Llama3.1-8B.

A year after its inception, OpenMed has achieved 340 million model downloads, offering over 1,500 open medical models under Apache 2.0, with 650+ capable of running on-device on iPhones.

Describes a medical speech-to-text system that runs locally on a MacBook, enabling streaming transcription without cloud dependency.

This paper applies ensemble machine learning models (Random Forest, Gradient Boosting, XGBoost, Extra Trees) to detect cirrhosis in hepatitis C patients using 28 features from 2038 Egyptian patients. The Extra Trees model achieved 96.92% accuracy with only 16 features, outperforming other models.