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Health technology assessment framework for artificial intelligence-based technologies.

Rossella Di Bidino1,2, Signe Daugbjerg1, Sara C Papavero1

  • 1Graduate School of Health Economics and Management, Universita Cattolica del SacroCuore (ALTEMS), 00168Rome, Italy.

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|November 21, 2024
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Summary
This summary is machine-generated.

Experts identified 48 critical topics for evaluating artificial intelligence (AI) health technologies (AIHTs) using health technology assessment (HTA). Key areas include AI model accuracy, patient safety, and data bias, highlighting the need for a new HTA framework.

Keywords:
AI-HTA frameworkAI-Mind Studyartificial intelligencehealth technology assessmentvalue assessment

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Area of Science:

  • Health Technology Assessment (HTA)
  • Artificial Intelligence (AI) in Healthcare
  • Medical Informatics

Background:

  • Artificial intelligence (AI)-based health technologies (AIHTs) are increasingly used in clinical settings.
  • Existing health technology assessment (HTA) frameworks lack standardization for AIHTs.
  • A comprehensive evaluation framework is needed for AIHTs.

Purpose of the Study:

  • To identify critical topics for a standardized HTA framework for AIHTs.
  • To determine expert consensus on essential components for evaluating AI in healthcare.
  • To inform the development of an HTA framework for AI-Mind project.

Main Methods:

  • A two-round Delphi survey was conducted with 46 experts.
  • Experts rated the significance of 65 topics for AIHT HTA.
  • Topics were categorized based on expert agreement scores, with 70% indicating high agreement.

Main Results:

  • 48 out of 65 proposed topics were deemed critical for an AIHT HTA framework.
  • Top critical topics include AI model accuracy (97.78%), patient safety (95.65%), ethical benefit-harm balance (95.56%), and data bias (91.30%).
  • The existing EUnetHTA Core Model is insufficient, with 14 of 20 additional topics identified as crucial.

Conclusions:

  • Establishing expert consensus on AI-relevant HTA topics is crucial for a robust assessment framework.
  • The developed framework will support the evaluation of AI tools for early dementia diagnosis.
  • This work is foundational for the European AI-Mind project.