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Is a score enough? Pitfalls and solutions for AI severity scores.

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Artificial intelligence (AI) scores in radiology have six human factor limitations impacting their usefulness. Providing false discovery and omission rates could mitigate these AI score limitations.

Keywords:
Artificial intelligenceBiasCognitionRadiologyReproducibility of results

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Area of Science:

  • Radiology
  • Artificial Intelligence (AI)
  • Psychological Science
  • Statistics

Background:

  • Artificial intelligence (AI) tools in radiology commonly provide severity scores, indicating pathology likelihood.
  • The utility and transparency of these AI-generated scores remain under-examined.
  • Existing research has not sufficiently addressed the radiologist-AI interaction.

Purpose of the Study:

  • To elucidate six human factors limitations of AI scores in radiology.
  • To propose a hypothesis for mitigating these limitations.
  • To discuss empirical testing of the proposed hypothesis.

Main Methods:

  • Analysis of AI score utility drawing on principles from psychological science and statistics.
  • Identification of six key human factors limitations: inter-AI variability, intra-AI variability, inter-radiologist variability, intra-radiologist variability, unknown score distribution, and perceptual challenges.
  • Formulation of a hypothesis involving false discovery rate (FDR) and false omission rate (FOR).

Main Results:

  • Six human factors limitations were identified that undermine the utility of AI severity scores.
  • These limitations include variability across and within AI systems and radiologists, unknown score distributions, and perceptual challenges.
  • A hypothesis is proposed: FDR and FOR thresholds can mitigate these limitations.

Conclusions:

  • The utility of AI severity scores in radiology is significantly constrained by human factors.
  • Addressing variability and perceptual challenges is crucial for effective radiologist-AI interaction.
  • Incorporating FDR and FOR as thresholds offers a potential strategy to enhance the reliability and utility of AI scores.