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Related Experiment Video

Updated: Aug 3, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Published on: June 13, 2025

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The artificial intelligence evidence-based medicine pyramid.

Valentina Bellini1, Federico Coccolini2, Francesco Forfori3

  • 1Department of Medicine and Surgery, University of Parma, Anesthesiology, Critical Care and Pain Medicine Division, Parma 43126, Italy.

World Journal of Critical Care Medicine
|April 10, 2023
PubMed
Summary

Artificial intelligence in intensive care shows promise, but a gap remains between research and practice. Robust validation studies by multidisciplinary teams are crucial for bridging this gap.

Keywords:
Artificial intelligenceClinical researchEvidence-based medicineIntensive careIntensive care unit

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Area of Science:

  • Critical Care Medicine
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Numerous studies explore artificial intelligence (AI) applications in intensive care units (ICUs).
  • A significant disconnect persists between AI research findings and their integration into routine clinical practice.
  • Bridging this gap requires rigorous validation of AI tools.

Purpose of the Study:

  • To highlight the existing gap between AI research and clinical practice in intensive care.
  • To emphasize the necessity of robust validation studies for AI implementation.
  • To advocate for multidisciplinary team involvement in AI validation.

Main Methods:

  • Literature review of AI in intensive care.
  • Analysis of the translational gap between research and practice.
  • Conceptual framework for multidisciplinary validation.

Main Results:

  • AI in intensive care is a growing research area.
  • Clinical implementation of AI tools is lagging behind research advancements.
  • Multidisciplinary validation is identified as a key requirement for successful AI adoption.

Conclusions:

  • Addressing the research-practice gap in intensive care AI requires dedicated validation efforts.
  • Multidisciplinary teams are essential for conducting robust validation studies.
  • Successful integration of AI into intensive care depends on overcoming current implementation barriers.