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Area of Science:

  • Medical Imaging Informatics
  • Artificial Intelligence in Healthcare
  • Patient Communication

Background:

  • Complex Magnetic Resonance Imaging (MRI) fistula reports pose challenges for patient comprehension.
  • Large Language Models (LLMs) show potential for translating technical medical information into accessible summaries.

Purpose of the Study:

  • To evaluate the clinical utility, safety, and patient acceptability of GPT-4o for generating patient-friendly MRI fistula report summaries.
  • To compare the readability, trustworthiness, usefulness, and comprehension of AI-generated summaries versus original MRI reports.

Main Methods:

  • A three-phase study involving prompt engineering, AI output review by a multidisciplinary panel, and patient evaluation of original versus AI-generated summaries.
  • Phase II assessed 250 MRI fistula reports for hallucinations and thematic content.
  • Phase III randomized 61 patients to compare AI summaries with original reports on readability, trust, usefulness, and comprehension.

Main Results:

  • AI-generated summaries significantly outperformed original reports in readability, comprehension, and usefulness (p < 0.001).
  • Hallucinations occurred in 11% of AI outputs; clinicians noted risks of inaccuracies despite improved language.
  • AI summaries had a higher Flesch-Kincaid score (66 vs. 26), indicating greater ease of understanding.

Conclusions:

  • LLMs can improve patient understanding of complex MRI reports but require structured oversight to mitigate risks like hallucinations and inconsistent terminology.
  • Safe implementation necessitates domain-specific refinement, clinician validation, and standardized reporting templates with lay summaries.
  • Future AI development should focus on clinician-approved, standardized templates for enhanced patient communication.