medical-ai

MedMix is a semantic-alignment framework for federated multimodal sparse Mixture-of-Experts that addresses modality heterogeneity by coordinating routing and expert specialization, achieving improved performance in medical AI datasets.

This article provides an overview of AI in drug discovery, discussing its current applications and future directions.

This paper proposes a YOLO- and CLIP-based vision-language framework to classify mosquito flight frames for Dengue virus detection, achieving 98.54% accuracy and 99.91% sensitivity at frame level, with complete video-level performance after temporal aggregation.

JiuZhen WenWen capability upgrade, expanding from health Q&A to continuous care, doing what general AI tools won't and can't do.

This Wired article explores how AI could help detect fatty liver disease earlier by analyzing electronic health records and lab reports, potentially enabling prevention and reversal of liver damage.

This paper introduces the Counterfactual Clinical Audit (CCA) framework to evaluate offline reinforcement learning agents for ICU sepsis management, exposing 'toxic mimicry' where agents replicate harmful treatment patterns that standard metrics miss. Using MIMIC-III data, it shows a Medical Decision Transformer fails to escalate vasopressors under rising lactate, while a causal transformer performs safely.

Introduces TAF-MED, a physician-reviewed benchmark of 500 multi-turn medical safety scenarios, showing that LLMs often collapse from safe initial refusals to unsafe responses when users declare self-treatment intent. Evaluation of eight LLMs across 4,000 conversations finds 71.6% contained unsafe responses and first-turn safety is an insufficient proxy for conversational safety persistence.

Google Research and Google DeepMind's AMIE medical AI system demonstrates expert-level real-time audio-visual clinical consultation capabilities in a first-of-its-kind randomized study, with clinical evaluators rating it favorably across core competencies.

Microsoft Research introduces CARE-X, a unified chest X-ray vision-language model that combines flexible reasoning, calibrated predictions, and tool-augmented measurement for clinically useful radiology interpretation.

Google has developed an AI doctor trained via online reinforcement learning in simulated medical residencies, achieving 88% diagnostic accuracy and being preferred over board-certified experts in 87.6% of blinded comparisons. The system handles long-horizon patient dialogues and adversarial simulated patients, marking a major leap in clinical AI.

A research paper presents a latent neural differential equation framework that infers unknown blood-clotting parameters from sparse measurements and forecasts thrombus growth, with stochastic neural ODEs achieving the best predictive performance.

Chinese researchers at BGI-Research released OneGenome, an open-source AI system that interprets gene mutations for clinical diagnosis, outperforming general LLMs like DeepSeek-v4, to help shorten the diagnostic odyssey for rare disease patients.

This paper introduces Guideline-as-Oracle (GAO), a method for zero-annotation training of a multi-turn ophthalmic telephone triage agent by compiling American Academy of Ophthalmology guidance into a 70-row rule table used to generate 3,000 training dialogues. Fine-tuning a 9B model on this corpus improves agreement with an operational reference from 61.7% to 74.1% and emergent-case recall from 9.5% to 69.0%, beating several general-purpose systems without needing a frontier model at inference.

Presents RESPClinBench, a real-world scenario benchmark for respiratory clinical decision-making, evaluating seven LLMs on COPD and pulmonary nodule cases. Finds task-specific limitations including imaging hallucination and medication-safety risks.

A pulmonologist discusses how AI is poised to take over aspects of his medical job, highlighting the growing impact of AI in healthcare diagnostics and clinical practice.

ClinFusion is a new open medical multimodal LLM from Alibaba DAMO Academy, available in 8B and 32B sizes (Apache 2.0), with unified 2D + 3D image understanding and state-of-the-art results on medical benchmarks.

This paper introduces PatTree, a graph-based multimodal patient representation that automatically structures heterogeneous clinical data for medical classification tasks. It achieves state-of-the-art performance on the ADNI-1 cohort with 98.5% balanced accuracy for Alzheimer's disease classification.

Introduces OncoTriad-QA, a patient-level benchmark integrating radiology, pathology, genomics, and clinical data for pan-cancer reasoning, along with OncoVLM, a reference multimodal model that outperforms existing medical LLMs after fine-tuning.

TumorBoard is a multi-agent decision-support system for longitudinal neuro-oncology that uses a shared longitudinal case state and auditable claim-evidence ledger. It outperforms baselines on a 360-case benchmark, with a safety governor reducing harmful recommendations.

Introduces MedPIC-Bench, a benchmark with counterfactual questions to evaluate whether LLMs correctly apply medication-safety rules when patient-specific conditions change; across 28 LLMs, accuracy drops significantly on counterfactual questions, revealing a common failure to revise judgments.