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Related Experiment Video

Updated: Apr 1, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Med.ai ASK: an agentic system for biomedical question answering.

Nhung T H Nguyen1, Dmytro S Lituiev1, Zhimin Liu1

  • 1Data Science and Digital Health, Innovative Medicine, Johnson & Johnson, Titusville, NJ, United States.

Journal of the American Medical Informatics Association : JAMIA
|March 30, 2026
PubMed
Summary

Med.ai ASK, an AI research co-pilot, provides accurate biomedical answers by dynamically retrieving information and using tool-driven reasoning. This agentic system enhances scientific knowledge discovery and reduces AI hallucinations.

Keywords:
agentic AIbiomedical question answeringlarge language modelreasoningretrieval-augmented generation

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Science
  • Natural Language Processing

Background:

  • Generative AI is revolutionizing scientific knowledge access.
  • Current AI solutions struggle with the nuance of biomedical research questions.
  • There is a need for reliable, grounded AI-powered biomedical information retrieval.

Purpose of the Study:

  • To develop Med.ai ASK, an agentic question-answering system for biomedical inquiries.
  • To enhance accuracy and reliability in AI-generated biomedical responses.
  • To create a system that parses complex research questions effectively.

Main Methods:

  • Utilized the ReAct framework and Self-Discover for tool-calling and reasoning.
  • Integrated multiple biomedical knowledge bases, vector databases, APIs, and NER tools.
  • Ingested 44 million biomedical documents and evaluated on diverse QA datasets.

Main Results:

  • Med.ai ASK demonstrates strong performance, accuracy, and reduced hallucinations compared to other AI solutions.
  • Human and LLM evaluations show aligned assessments, supporting its reliability.
  • The agent is interpretable, effectively selects tools, and is deployed in a production platform with over 1600 users.

Conclusions:

  • Med.ai ASK dynamically integrates information retrieval tools for accurate and factual biomedical answers.
  • The agentic design provides robust interpretative capabilities crucial for the biomedical domain.
  • This system represents a significant advancement in AI-assisted scientific research.