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Recognising errors in AI implementation in radiology: A narrative review.

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Artificial intelligence (AI) failures in radiology can hinder performance and adoption. This review identifies issues in AI models, infrastructure, and human factors to propose solutions for better AI integration.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Clinical Implementation

Background:

  • Artificial intelligence (AI) implementation in clinical settings, particularly radiology, faces numerous challenges.
  • Failures can negatively affect AI algorithm performance, clinical adoption, workflow efficiency, and cost-effectiveness.

Purpose of the Study:

  • To comprehensively review and discuss the diverse reasons for AI failures in radiology.
  • To analyze published evidence concerning AI models, technical infrastructure, and human factors in AI implementation.

Main Methods:

  • This narrative review synthesizes existing literature on AI failures in radiology.
  • Analysis focuses on three core components: AI models (lifecycle), technical infrastructure (hardware/software), and human factors.

Main Results:

  • AI failures stem from issues within the AI models themselves, the supporting technical infrastructure, and human-related elements.
  • Specific examples of failures across these domains are detailed.

Conclusions:

  • Understanding the root causes of AI failures is crucial for optimizing its use in radiology.
  • Proposed solutions aim to enhance the successful adoption and integration of AI tools in clinical practice.