Browsing by Subject "short prompt"

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    Enhancing Patient Communication With Chat-GPT in Radiology: Evaluating the Efficacy and Readability of Answers to Common Imaging-Related Questions
    (Elsevier, 2023) Gordon, Emile B.; Towbin, Alexander J.; Wingrove, Peter; Shafique, Umber; Haas, Brian; Kitts, Andrea B.; Feldman, Jill; Furlan, Alessandro; Radiology and Imaging Sciences,School of Medicine
    Purpose To assess ChatGPT's accuracy, relevance, and readability in answering patients' common imaging-related questions and examine the effect of a simple prompt. Methods 22 imaging-related questions were developed from categories previously described as important to patients: safety, the radiology report, the procedure, preparation before imaging, meaning of terms, and medical staff. These questions were posed to ChatGPT with and without a short prompt instructing the model to provide an accurate and easy-to-understand response for the average person. Four board-certified radiologists evaluated the answers for accuracy, consistency, and relevance. Two patient advocates also reviewed responses for their utility for patients. Readability was assessed by Flesch Kincaid Grade Level (FKGL). Statistical comparisons were performed using chi-square and paired t-tests. Results 264 answers were assessed for both unprompted and prompted questions. Unprompted responses were accurate 83% (218/264) of the time, which did not significantly change for prompted responses (87% [229/264]; P=0.2). The consistency of the responses increased from 72%f (63/88) to 86% (76/88) when prompted (P=0.02). Nearly all responses (99% [261/264]) were at least partially relevant for both question types. Fewer unprompted responses were considered fully relevant at 67% (176/264), though this increased significantly to 80% when prompted (210/264) (P=0.001). The average FKGL was high at 13.6 [12.9-14.2], unchanged with the prompt (13.0 [12.41-13.60], P=0.2). None of the responses reached the eighth-grade readability recommended for patient-facing materials. Conclusions ChatGPT demonstrates the potential to respond accurately, consistently, and relevantly to patients' imaging-related questions. However, imperfect accuracy and high complexity necessitate oversight before implementation. Prompts reduced response variability and yielded more targeted information but did not improve readability. Relevance and Application ChatGPT has the potential to increase accessibility to health information and to streamline the production of patient-facing educational materials, though its current limitations require cautious implementation and further research.