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

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.