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Beyond Pattern Recognition: A Gödelian Limit on Self-Validation in Radiologic Artificial Intelligence.

Tugce Miroglu Guler1, Pablo R Ros2, Sukru Mehmet Erturk3

  • 1Department of Radiological Sciences, Institute of Health Sciences, İstanbul University, İstanbul, Turkey; Department of Radiology, Haydarpasa Numune Training and Research Hospital, İstanbul, Turkey.

Journal of the American College of Radiology : JACR
|February 21, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) systems show promise in radiology but cannot validate their own outputs. Radiologists remain essential for ensuring AI clinical validity, appropriateness, and ethical actionability.

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Area of Science:

  • Radiology
  • Medical Artificial Intelligence
  • Clinical Informatics

Background:

  • Artificial intelligence (AI) demonstrates human-level performance in specific radiological tasks, leading to increased clinical adoption.
  • A critical limitation of current AI is its inability to self-validate outputs for clinical appropriateness and ethical actionability in real-world settings.

Purpose of the Study:

  • To analyze the inherent limitations of AI in clinical validation within radiology.
  • To redefine the radiologist's role as a crucial validator and integrator of AI technologies.
  • To explore the implications of AI validation for radiology's future.

Main Methods:

  • Conceptual analysis using Gödel's incompleteness theorem as a metaphorical framework.
  • Discussion of the structural limitations of statistical learning systems in open clinical environments.
  • Examination of the radiologist's role in technical, clinical, and ethical validation.

Main Results:

  • AI validation cannot be fully internalized within statistical learning systems due to the open nature of clinical environments.
  • The radiologist's role as a validator and integrator is fundamental and enduring.
  • AI's limitations necessitate a re-evaluation of its deployment, governance, education, and reimbursement in radiology.

Conclusions:

  • The inability of AI to independently validate its outputs is a structural, not temporary, limitation.
  • Radiologists are indispensable for ensuring the safe and effective integration of AI in clinical practice.
  • Recognizing validation as a core competency is vital for the future of radiology and AI governance.