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Generative artificial intelligence chatbots may provide appropriate informational responses to common vascular surgery questions by patients.
Chervonski, Ethan; Harish, Keerthi B; Rockman, Caron B; Sadek, Mikel; Teter, Katherine A; Jacobowitz, Glenn R; Berland, Todd L; Lohr, Joann; Moore, Colleen; Maldonado, Thomas S.
Affiliation
  • Chervonski E; New York University Grossman School of Medicine, New York, NY, USA.
  • Harish KB; New York University Grossman School of Medicine, New York, NY, USA.
  • Rockman CB; Division of Vascular & Endovascular Surgery, Department of Surgery, New York University Langone Health, New York, NY, USA.
  • Sadek M; Division of Vascular & Endovascular Surgery, Department of Surgery, New York University Langone Health, New York, NY, USA.
  • Teter KA; Division of Vascular & Endovascular Surgery, Department of Surgery, New York University Langone Health, New York, NY, USA.
  • Jacobowitz GR; Division of Vascular & Endovascular Surgery, Department of Surgery, New York University Langone Health, New York, NY, USA.
  • Berland TL; Division of Vascular & Endovascular Surgery, Department of Surgery, New York University Langone Health, New York, NY, USA.
  • Lohr J; Dorn Veterans Affairs Medical Center, Columbia, SC, USA.
  • Moore C; InVein Clinic, Cape Girardeau, MO, USA.
  • Maldonado TS; Division of Vascular & Endovascular Surgery, Department of Surgery, New York University Langone Health, New York, NY, USA.
Vascular ; : 17085381241240550, 2024 Mar 18.
Article in En | MEDLINE | ID: mdl-38500300
ABSTRACT

OBJECTIVES:

Generative artificial intelligence (AI) has emerged as a promising tool to engage with patients. The objective of this study was to assess the quality of AI responses to common patient questions regarding vascular surgery disease processes.

METHODS:

OpenAI's ChatGPT-3.5 and Google Bard were queried with 24 mock patient questions spanning seven vascular surgery disease domains. Six experienced vascular surgery faculty at a tertiary academic center independently graded AI responses on their accuracy (rated 1-4 from completely inaccurate to completely accurate), completeness (rated 1-4 from totally incomplete to totally complete), and appropriateness (binary). Responses were also evaluated with three readability scales.

RESULTS:

ChatGPT responses were rated, on average, more accurate than Bard responses (3.08 ± 0.33 vs 2.82 ± 0.40, p < .01). ChatGPT responses were scored, on average, more complete than Bard responses (2.98 ± 0.34 vs 2.62 ± 0.36, p < .01). Most ChatGPT responses (75.0%, n = 18) and almost half of Bard responses (45.8%, n = 11) were unanimously deemed appropriate. Almost one-third of Bard responses (29.2%, n = 7) were deemed inappropriate by at least two reviewers (29.2%), and two Bard responses (8.4%) were considered inappropriate by the majority. The mean Flesch Reading Ease, Flesch-Kincaid Grade Level, and Gunning Fog Index of ChatGPT responses were 29.4 ± 10.8, 14.5 ± 2.2, and 17.7 ± 3.1, respectively, indicating that responses were readable with a post-secondary education. Bard's mean readability scores were 58.9 ± 10.5, 8.2 ± 1.7, and 11.0 ± 2.0, respectively, indicating that responses were readable with a high-school education (p < .0001 for three metrics). ChatGPT's mean response length (332 ± 79 words) was higher than Bard's mean response length (183 ± 53 words, p < .001). There was no difference in the accuracy, completeness, readability, or response length of ChatGPT or Bard between disease domains (p > .05 for all analyses).

CONCLUSIONS:

AI offers a novel means of educating patients that avoids the inundation of information from "Dr Google" and the time barriers of physician-patient encounters. ChatGPT provides largely valid, though imperfect, responses to myriad patient questions at the expense of readability. While Bard responses are more readable and concise, their quality is poorer. Further research is warranted to better understand failure points for large language models in vascular surgery patient education.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Vascular Journal subject: ANGIOLOGIA / CARDIOLOGIA Year: 2024 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Vascular Journal subject: ANGIOLOGIA / CARDIOLOGIA Year: 2024 Document type: Article Affiliation country: United States