medical-ai

Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing.

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

[TMLR 2025🔥] A survey for the autoregressive models in vision.

[MICCAI 2023] MedNeXt is a fully ConvNeXt architecture for 3D medical image segmentation.

MedResearcher-R1 is a deep research agent for medical scenarios, built on a knowledge-informed trajectory synthesis framework.

Build a complete experiment pipeline for your PyTorch MIP model in 10 seconds.

[Nature Communications 2025] Towards Expert-level Autonomous Carotid Ultrasonography with Large-scale Learning-based Robotic System

Agent Skills for medical research — literature search, reporting-guideline & citation checks, statistics, publication figures, submission. Works with Claude Code, Codex, Cursor & GitHub Copilot. Built by a physician-researcher, tested on real publications. MIT.

OpenLens AI: A Fully Autonomous Multimodal Research Agent｜ OpenLens AI：全自主多模态科研智能体

[Nature Medicine'25] A Multimodal Vision Foundation Model for Clinical Dermatology

Frontiers in Intelligent Colonoscopy [ColonSurvey | ColonINST | ColonGPT]

[ICML 2026] Official UniMedVL implementation — a unified medical vision-language model for multimodal understanding, medical image generation, and interleaved tasks in a single checkpoint, trained on UniMed-5M (5.6M+ samples).

ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in Medicine

[JAMA] "Complete AI-Enabled Echocardiography Interpretation with Multitask Deep Learning" by Gregory Holste, Evangelos K. Oikonomou, Márton Tokodi, Attila Kovács, Zhangyang Wang, & Rohan Khera

Open-source multimodal dataset: 98K+ clinical cases & 139K+ medical images from PubMed Central

[ICCV'25 Highlight] Derm1M: A Million‑Scale Vision‑Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology

This repository houses machine learning models and pipelines for predicting various diseases, coupled with an integration with a Large Language Model for Diet and Food Recommendation. Each disease prediction task has its dedicated directory structure to maintain organization and modularity.

Unofficial OpenEvidence MCP server — query OpenEvidence from Claude Code, Codex, and any MCP client through your own logged-in browser tab (no API key). Fire-and-forget asks, a shared relay daemon for concurrent sessions, BibTeX + Crossref citations.

The source code and data link of paper "MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare".

This repository is a summary of "AI for Medicine" lectured by Deeplearning.ai, Coursera. (https://www.coursera.org/specializations/ai-for-medicine)