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Healthcare Agent Orchestrator is a multi-agent accelerator that coordinates modular specialized agents across diverse data types and tools like M365 and Teams to assist multi-disciplinary healthcare workflows—such as cancer care.
The main features of azure-samples/healthcare-agent-orchestrator are: Clinical Decision Support.
Projects with overlapping indexed features include: scir-hi/huatuo-llama-med-chinese — Huatuo-Llama-Med-Chinese is a medical large language model specialized in processing and generating natural language… openemr/openemr — OpenEMR is an open-source electronic health record (EHR) system that also functions as a medical practice management… k-dense-ai/scientific-agent-skills — This project is a collection of specialized toolkits and an agent skill library designed to equip large language model… bowang-lab/medrax — MedRAX: Medical Reasoning Agent for Chest X-ray - ICML 2025. geteff1/multi-agent-conversation-for-disease-diagnosis — This repository presents a novel multi-agent conversation framework designed to enhance the capabilities of Large… gersteinlab/medagents-benchmark — MedAgentsBench: Benchmarking Thinking Models and Agent Frameworks for Complex Medical Reasoning.
OpenEMR is an open-source electronic health record (EHR) system that also functions as a medical practice management platform and a patient portal, all integrated with standards-based health data exchange. It stores and manages patient health records, handles clinical workflows, supports scheduling and billing, and provides patients with secure self-service access to their information. Interoperability is built in via FHIR and C-CDA for exchanging records with external systems and Direct protocol for encrypted provider messaging. The system is designed to be extensible, with a modular plugin
Huatuo-Llama-Med-Chinese is a medical large language model specialized in processing and generating natural language text in Chinese. It is an instruction-tuned system designed to answer professional healthcare questions by leveraging a dedicated medical knowledge base. The model integrates structured medical literature and knowledge graphs to ensure clinical accuracy during response generation. It employs knowledge-graph augmented inference to combine structured entity relationships with neural network outputs. The system is developed through domain-specific weight adaptation, cross-lingual
This project is a collection of specialized toolkits and an agent skill library designed to equip large language model agents with the capabilities to perform complex scientific research across biology, chemistry, medicine, and physics. It provides a structured framework of integration paths and tools that allow agents to execute multi-step research workflows. The system is distinguished by its domain-specific toolsets, including a bioinformatics toolkit for genomic and single-cell analysis, a cheminformatics toolset for drug-target binding and lead compound optimization, and a multi-omics an
MedRAX: Medical Reasoning Agent for Chest X-ray - ICML 2025