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Leveraging Generative AI for Clinical Evidence Synthesis Needs to Ensure Trustworthiness.

Gongbo Zhang1, Qiao Jin2, Denis Jered McInerney3

  • 1Columbia University, Department of Biomedical Informatics, New York, 10032, US.

Arxiv
|October 1, 2025
PubMed
Summary
This summary is machine-generated.

Generative AI can aid evidence-based medicine by synthesizing clinical data. However, ensuring AI models are trustworthy, fair, and inclusive is crucial for reliable automated evidence synthesis.

Keywords:
clinical evidence synthesisevidence-based medicinelarge language modelstrustworthy generative AI

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Evidence-Based Medicine

Background:

  • Evidence-based medicine (EBM) enhances healthcare quality by integrating best evidence into clinical practice.
  • The exponential growth of clinical evidence presents significant challenges in data collection, appraisal, and synthesis.
  • Generative Artificial Intelligence (AI), including large language models (LLMs), offers potential solutions for managing this evidence explosion.

Purpose of the Study:

  • To explore the role of generative AI in automating evidence synthesis for EBM.
  • To discuss the challenges and considerations for developing trustworthy AI models in this domain.

Main Methods:

  • This perspective reviews current advancements in generative AI relevant to evidence synthesis.
  • It analyzes the requirements for accountability, fairness, and inclusivity in AI models used for clinical decision support.

Main Results:

  • Generative AI shows promise in streamlining the collection, appraisal, and synthesis of clinical evidence.
  • Significant challenges remain in developing AI systems that are accountable, fair, and inclusive.

Conclusions:

  • Generative AI has the potential to revolutionize evidence synthesis in EBM.
  • Further research and development are needed to ensure the trustworthiness and ethical application of AI in healthcare.