medical-ai

ClinSeekAgent is an automated agentic framework that enables large language models to actively acquire and synthesize multimodal clinical evidence from raw data sources, improving decision-making accuracy in both text-only and multimodal tasks. It introduces the ClinSeek-Bench benchmark and a distilled model ClinSeek-35B-A3B that achieves strong performance on agentic clinical reasoning.

Introduces Fully Open Meditron, the first fully open pipeline for building clinical LLMs, featuring a clinician-audited training corpus and reproducible framework, achieving state-of-the-art among fully open medical specialist models.

DeepMind's Co-Scientist AI helped identify two repurposed medicines that block liver fibrosis in lab tests, including a cancer drug that blocked 91% of a damage response, outperforming human expert selections.

This paper presents an AI-based model for fetal heart rate monitoring that reconstructs signals, analyzes variability, and detects decelerations/accelerations with high sensitivity and specificity.

DT-Transformer is a foundation model trained on 57.1 million structured EHR entries from 1.7 million patients across 11 hospitals in the Mass General Brigham health system, achieving strong discrimination for next-event prediction across 896 disease categories.

This paper defines and measures premature closure in frontier LLMs, finding that models frequently give confident answers even when the correct option is removed or when clarification is needed, highlighting a critical safety concern for medical applications.

Ontario's auditor general found that AI transcription tools for doctors generated errors and hallucinations, potentially harming patient care, and criticized inadequate government testing.

This paper introduces Checkup2Action, a multimodal dataset and benchmark for generating patient-oriented action cards from clinical check-up reports, addressing the interpretability gap for laypersons.

This paper introduces ClinicalBench and the EpiKG system, evaluating assertion-aware retrieval for clinical question answering on MIMIC-IV data across multiple LLMs. It demonstrates that handling negation and temporality in retrieval significantly improves performance over standard baselines.

AntAngelMed is a newly open-sourced 100B-parameter medical language model developed by Zhejiang Health Information Center, Ant Healthcare, and Anzhen'er Medical AI. It achieves top rankings on HealthBench and MedAIBench, utilizing efficient MoE architecture for high-performance inference.

Zhejiang Health and Ant Healthcare released AntAngelMed, an open-source 100B parameter medical LLM that ranks top on MedBench and supports efficient local inference with high privacy.

This paper introduces MedAction, a framework for training LLMs on active, multi-turn clinical diagnosis by simulating iterative test ordering and hypothesis updates. It presents a new dataset, MedAction-32K, and demonstrates state-of-the-art performance for open-source models on medical benchmarks.

This paper introduces STDA-Net, a domain adaptation framework for cross-dataset sleep stage classification using 2D spectrograms and adversarial learning. It demonstrates improved accuracy and stability over existing 1D EEG baseline methods on public datasets.

The paper introduces MedExAgent, a framework that formalizes clinical diagnosis as a Partially Observable Markov Decision Process (POMDP) to handle noisy and incomplete information. It proposes a two-stage training pipeline combining supervised finetuning and reinforcement learning to improve diagnostic accuracy and cost-efficiency in medical LLMs.

Report claims that GPT-5.5 Instant shows significant improvements in factual accuracy, particularly in high-stakes fields like medicine, law, and finance.

A new AI model (REDMOD) can detect pancreatic cancer up to three years earlier than human doctors by analyzing CT scans for subtle irregularities, potentially improving early diagnosis and survival rates.

This paper presents an automated diagnostic system for grading knee osteoarthritis severity using an optimized ResNet-18 model deployed on edge devices via TensorFlow Lite. It integrates an LLM interface using Gemini 2.0 Flash to provide structured interpretive findings while maintaining offline capability for resource-constrained environments.

This paper investigates whether linearly decodable failure signals in LLM hidden states can be corrected via residual-stream steering. It finds that while 'overthinking' failures are decodable, fixed linear steering fails to correct them due to representational entanglement with task-critical computations, though the probes effectively support selective abstention.

The paper introduces CXR-MAX, a large-scale benchmark for evaluating reasoning alignment in non-stationary environments using X-ray data from multiple MLLMs.

Introduces CGM-JEPA, a self-supervised pretraining framework for continuous glucose monitor data that improves cross-modal and cross-cohort performance through masked latent prediction and distributional objectives.