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Use First, Trust Later? Exploring How Health Care Providers View the Gaps Between AI's Regulation and Its

Aviad Raz1, Yael Inbar2, Ziv Paz3

  • 1Department of Sociology and Anthropology, Ben-Gurion University of the Negev, Be'er-Sheva, Be'er-Sheva, 8496500, Israel, 972 86472058.

JMIR Formative Research
|November 10, 2025
PubMed
Summary
This summary is machine-generated.

Healthcare providers need to continuously monitor artificial intelligence (AI) during early adoption. This oversight is crucial because current regulations focus on premarket approval, neglecting AI's real-world adaptability and life cycle potential.

Keywords:
artificial intelligencecontent analysishealth care managementimplementationqualitativeradiologyregulation

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Area of Science:

  • Health informatics
  • Artificial intelligence in healthcare
  • Regulatory science

Background:

  • Current regulatory frameworks for artificial intelligence (AI) in healthcare primarily emphasize premarket approval.
  • This focus may inadvertently limit the consideration of AI's full life cycle and its potential for real-world adaptation.
  • The dynamic nature of AI necessitates a broader regulatory perspective beyond initial approval.

Purpose of the Study:

  • To highlight the limitations of a premarket approval-centric regulatory approach for AI in healthcare.
  • To emphasize the need for continuous oversight and quality assurance of AI systems by healthcare providers.
  • To underscore the importance of managing AI's real-world adaptiveness and life cycle potential.

Main Methods:

  • Qualitative analysis of current regulatory trends in healthcare AI.
  • Review of literature on AI life cycle management and real-world performance.
  • Conceptual framework development for ongoing AI oversight in clinical settings.

Main Results:

  • Regulatory focus on premarket approval overlooks AI's evolving nature and real-world performance.
  • Healthcare providers-cum-deployers are essential for continuous quality assurance and oversight.
  • Early adoption phases are particularly critical for managing AI adaptiveness and ensuring patient safety.

Conclusions:

  • Ongoing oversight and quality assurance by healthcare providers are imperative for AI in healthcare.
  • Regulatory strategies must evolve to encompass the entire life cycle of AI, including real-world performance and adaptability.
  • A balanced approach integrating premarket evaluation with continuous post-deployment monitoring is necessary for safe and effective AI integration.