Best Tools for Precision Medicine

This blog highlights the launch and significance of Microsoft’s Healthcare Agent Orchestrator, a powerful AI-driven platform designed to support complex, multidisciplinary medical workflows—most notably in cancer care. Key Significance:    •   Cancer treatment is highly personalized, but <1% of patients currently benefit from fully personalized care plans due to the high cost, time, and effort involved.    •   Multimodal Agentic AI can dramatically reduce the hours clinicians spend on reviewing complex patient data.    •   Microsoft’s platform enables orchestrated collaboration among specialized AI agents to streamline these workflows and integrate into tools clinicians already use (e.g., Microsoft Teams, Word, Copilot).    •   The goal is to scale precision medicine, speed up decision-making, and augment—rather than replace—human experts. Examples of Specialized Agents: 1. Patient History Agent – Builds a chronological patient timeline using Universal Medical Abstraction. 2. Radiology Agent – Provides a “second read” of medical imaging, using models like CXRReportGen/MAIRA-2. 3. Pathology Agent – Can link with external pathology agents like Paige.ai’s Alba, analyzing tumor slides. 4. Cancer Staging Agent – Applies AJCC clinical guidelines to accurately determine cancer stages. 5. Clinical Guidelines Agent – Uses NCCN guidelines to recommend treatments. 6. Clinical Trials Agent – Matches patients to trials, improving recall over baseline models. 7. Medical Research Agent – Synthesizes research findings into actionable clinical insights. 8. Report Creation Agent – Generates integrated, formatted reports for tumor boards. Real-World Impact & Collaborators:    •   Stanford Health Care, Johns Hopkins, UW Health, Mass General Brigham, and Providence Genomics are actively piloting or integrating these agents.    •   Real use cases include enhancing tumor board meetings, streamlining clinical trial matching, and deepening pathology insight via conversational interfaces (e.g., Paige.ai’s Alba in preview). Bottom Line: The healthcare agent orchestrator marks a pivotal step in democratizing precision oncology, accelerating collaboration between AI and human experts, and scaling care excellence through modular, customizable AI agents. It’s already in the hands of top institutions and could revolutionize how we approach cancer treatment at scale.

New AI tool can help select the most suitable treatment for cancer patients: 🧬The tool DeepPT developed by the National Cancer Institute (NCI) in America and Pangea Biomed works by predicting a patient's messenger RNA (mRNA) profile 🧬This mRNA - essential for protein production - is also the key molecular information for personalised cancer medicine 🧬Impressively, if the tool predicted that a patient would respond to a given therapy, they would be Two to Five times more likely to respond than a patient who was predicted not to respond to it 🧬The inputs are histopathology images, essentially stained slides of patient tumour tissue, which are routinely available, cheap and fast to process - reducing delays associated with traditional molecular data processing. 🧬DeepPT was trained on over 5,500 patients across 16 prevalent cancer types, including breast, lung, head and neck, cervical and pancreatic cancers 👇Link to articles and study in comments #digitalhealth #AI

🚀 Introducing TxAgent: the first of its kind AI agent for therapeutic reasoning across a universe of tools Thrilled to introduce TxAgent, an AI agent that redefines how AI can reason, retrieve, and integrate biomedical knowledge for precision therapeutics, led by a postdoc, Shanghua Gao 🔍 Beyond prediction—reasoning AI for medicine TxAgent is not just another predictive model. It is the first AI system designed to think through therapeutic problems, iteratively query external sources, and generate transparent, step-by-step reasoning traces. By integrating real-time biomedical knowledge, TxAgent's treatment recommendations are accurate and continuously updated. 🔗 Benchmarking TxAgent against 671B DeepSeek-R1 We benchmarked TxAgent against DeepSeek-R1 (671B, NVIDIA) and other leading AI models. The results? TxAgent outperformed much larger LLMs in multi-step therapeutic reasoning, achieving up to 92.1% accuracy in drug selection, treatment personalization, and therapeutic reasoning. 🏥 What’s Inside the 211 tools in TxAgent’s ToolUniverse? ✅ All FDA-approved drugs since 1939 – Includes drug mechanisms, indications, contraindications, dosing, safety warnings, and pharmacokinetics from FDA drug labels and OpenFDA ✅ Clinical insights from Open Targets – Provides up-to-date drug-disease, phenotype, and molecular target associations used in precision medicine ✅ Pharmacology – Covers drug-drug interactions, metabolic pathways, and contraindications based on comorbidities and concurrent medications ✅ Personalized treatment guidelines – Assesses patient-specific factors such as age, pregnancy, renal function, and genetic variations. Simultaneously assesses molecular, pharmacokinetic, and clinical-level interactions. Evaluates patient factors like genetics, comorbidities, and disease stage ✅ Real-time retrieval – Queries latest treatment indications, regulatory approvals from continuously updated sources 🔥 Key features: - Reasoning over retrieval – Moves beyond RAG-based retrieval to structured, multi-step decision-making - Tool-augmented AI – Interacts with 211 biomedical tools - Real-time knowledge integration and continuous learning – Responses are always grounded in up-to-date clinical knowledge. No outdated medical knowledge by always integrating live sources - Dynamic tool selection – Adapts its reasoning by choosing the most relevant tools in real time - Grounded medical AI – Reduces the risk of hallucinations, verifies every step of the way, and aligns recommendations with clinical guidelines 👉 Preprint: https://lnkd.in/e2rgn-rN 👉 TxAgent: https://lnkd.in/ef39JXs6 👉 Open code: https://lnkd.in/e69mBPjs 👉 AI toolbox: https://lnkd.in/eqdym_Gp Harvard University Harvard Medical School Department of Biomedical Informatics Harvard Data Science Initiative Kempner Institute at Harvard University Broad Institute of MIT and Harvard Massachusetts Institute of Technology NVIDIA AI