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Updated: Jul 2, 2026

A Point-of-Care Method with Integrated Decision Support Tool to Estimate Anemia at Population Level
05:35

A Point-of-Care Method with Integrated Decision Support Tool to Estimate Anemia at Population Level

Published on: January 19, 2024

Clinical decision support in hematological malignancies using a case-grounded AI agent.

Julian Zoller1,2,3,4, Michael Kalz1,2,3,4, Xuewei Wu5,6

  • 1JRG Hematology and Immune Engineering, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Nature Medicine
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

HemaGuide, an AI agent for hematologic malignancies, improves decision-making by structuring cases and grounding recommendations in guidelines and past cases. It offers real-time support with high accuracy, enhancing clinical concordance and physician performance.

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Hematology
  • Clinical Decision Support Systems

Background:

  • Multidisciplinary tumor boards are crucial for treating hematologic malignancies, integrating complex data for optimal patient care.
  • Uneven access to specialized subspecialty deliberation challenges timely and effective treatment planning.

Purpose of the Study:

  • To develop and evaluate HemaGuide, a locally deployable large language model agent for clinical decision support in hematologic malignancies.
  • To assess HemaGuide's ability to structure clinical data, route cases to specialized decision modes, and ground recommendations in evidence.

Main Methods:

  • HemaGuide, a modular large language model agent, converts unstructured clinical notes into structured case representations.
  • Cases are autonomously routed to 'guideline,' 'advanced,' or 'molecular' decision modes, grounded in disease-specific flowcharts and a memory of >2,000 tumor board cases.
  • Benchmarking involved expert-blinded evaluation on 45 high-complexity cases, automated variant classification, simulated practice studies, and external validation on 555 independent cases.

Main Results:

  • HemaGuide significantly improved concordance with tumor board decisions in expert-blinded benchmarking.
  • Automated classification of 70 missense variants showed high concordance with expert standards, with no oncogenic variants downgraded.
  • Agent-assisted resident physicians achieved near-senior concordance, and external validation showed 81.8% concordance across 47 entities.

Conclusions:

  • Locally deployable, case-grounded large language model agents like HemaGuide can provide auditable clinical decision support for hematologic malignancies.
  • HemaGuide demonstrates high concordance across institutions and under real-time conditions on commodity hardware, with a very low hallucination rate (0.3%).
  • The system enhances clinical decision-making efficiency and accuracy, potentially improving patient outcomes in hematologic oncology.