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DOI 10.1016/j.jacr.2023.06.009

Медицинская библиотека · Journal of the American College of Radiology : JACR · 2023

ChatGPT-4 Assistance in Optimizing Emergency Department Radiology Referrals and Imaging Selection.

Barash Yiftach, Klang Eyal, Konen Eli, Sorin Vera

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Аннотация

PURPOSE: The quality of radiology referrals influences patient management and imaging interpretation by radiologists. The aim of this study was to evaluate ChatGPT-4 as a decision support tool for selecting imaging examinations and generating radiology referrals in the emergency department (ED).

METHODS

Five consecutive clinical notes from the ED were retrospectively extracted, for each of the following pathologies: pulmonary embolism, obstructing kidney stones, acute appendicitis, diverticulitis, small bowel obstruction, acute cholecystitis, acute hip fracture, and testicular torsion. A total of 40 cases were included. These notes were entered into ChatGPT-4, requesting recommendations on the most appropriate imaging examinations and protocols. The chatbot was also asked to generate radiology referrals. Two independent radiologists graded the referral on a scale ranging from 1 to 5 for clarity, clinical relevance, and differential diagnosis. The chatbot's imaging recommendations were compared with the ACR Appropriateness Criteria (AC) and with the examinations performed in the ED. Agreement between readers was assessed using linear weighted Cohen's κ coefficient.

RESULTS

ChatGPT-4's imaging recommendations aligned with the ACR AC and ED examinations in all cases. Protocol discrepancies between ChatGPT and the ACR AC were observed in two cases (5%). ChatGPT-4-generated referrals received mean scores of 4.6 and 4.8 for clarity, 4.5 and 4.4 for clinical relevance, and 4.9 from both reviewers for differential diagnosis. Agreement between readers was moderate for clinical relevance and clarity and substantial for differential diagnosis grading.

CONCLUSIONS

ChatGPT-4 has shown potential in aiding imaging study selection for select clinical cases. As a complementary tool, large language models may improve radiology referral quality. Radiologists should stay informed about this technology and be mindful of potential challenges and risks.

Для цитирования: Barash Yiftach, Klang Eyal, Konen Eli, Sorin Vera. ChatGPT-4 Assistance in Optimizing Emergency Department Radiology Referrals and Imaging Selection.. Journal of the American College of Radiology : JACR. 2023. DOI: 10.1016/j.jacr.2023.06.009.

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