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A Taxonomy of Machine Hallucination in Radiology.

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Defining machine hallucination in generative AI is crucial for radiology. This study clarifies differing interpretations to improve AI trustworthiness and communication for clinical use.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology AI Evaluation
  • Generative AI Performance Metrics

Background:

  • Assessing generative AI in radiology requires clear definitions of machine hallucination.
  • Current ambiguity in hallucination definitions hinders reliable AI evaluation and clinical adoption.
  • Disparate interpretations of hallucination lead to conflicting AI system characterizations.

Purpose of the Study:

  • To provide a non-technical explanation of differing machine hallucination concepts in AI.
  • To propose a taxonomy of machine hallucination relevant to radiologists.
  • To reduce ambiguity in evaluating generative AI performance in radiology.

Main Methods:

  • Explanation of the fundamental disparity between two notions of machine hallucination.
  • Development of a taxonomy using radiologist-familiar terms.
  • Delineation of output contingencies for deployed AI systems.

Main Results:

  • Clarification of the core differences in machine hallucination definitions.
  • A proposed taxonomy to categorize AI outputs based on interpretation.
  • Framework to align AI evaluation with clinical use cases.

Conclusions:

  • A clear taxonomy reduces ambiguity in generative AI hallucination assessment.
  • Standardized definitions facilitate better communication among stakeholders.
  • Improved understanding enhances the trustworthiness and utility of AI in radiology.