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Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter

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Large language models like GPT-4 can simplify complex oncology radiology reports, improving patient understanding and communication efficiency. This AI application enhances doctor-patient interactions and potentially improves health outcomes.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Physician-patient communication is vital in oncology, yet radiology reports contain technical jargon hindering patient comprehension.
  • This complexity impacts patient engagement, decision-making, and overall care.
  • Large language models (LLMs) offer a potential solution for simplifying medical information.

Purpose of the Study:

  • To evaluate the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports.
  • To enhance physician-patient communication through clearer report interpretation.

Main Methods:

  • A retrospective analysis of 698 malignant tumor radiology reports was conducted.
  • GPT-4 generated simplified interpretative radiology reports (IRRs) from 70 selected reports.
  • Radiologists verified report consistency, while lay volunteers and physicians assessed readability and communication efficiency.

Main Results:

  • Simplified IRRs increased in word count but were read faster and more easily by volunteers.
  • Patient comprehension scores significantly improved after reading simplified reports.
  • Physician-patient communication time decreased substantially, indicating enhanced efficiency.

Conclusions:

  • LLMs, specifically GPT-4, show significant potential in simplifying oncological radiology reports.
  • Simplified reports improve patient understanding and streamline doctor-patient interactions.
  • AI applications like this can enhance healthcare communication and patient outcomes.