Evaluating ChatGPT as a patient resource for frequently asked questions about lung cancer surgery—a pilot study

Dana Ferrari-Light, Robert E. Merritt, Desmond D'Souza, Mark K. Ferguson, Sebron Harrison, Maria Lucia Madariaga, Benjamin E. Lee, Susan D. Moffatt-Bruce, Peter J. Kneuertz

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Objective: Chat-based artificial intelligence programs like ChatGPT are reimagining how patients seek information. This study aims to evaluate the quality and accuracy of ChatGPT-generated answers to common patient questions about lung cancer surgery. Methods: A 30-question survey of patient questions about lung cancer surgery was posed to ChatGPT in July 2023. The ChatGPT-generated responses were presented to 9 thoracic surgeons at 4 academic institutions who rated the quality of the answer on a 5-point Likert scale. They also evaluated if the response contained any inaccuracies and were prompted to submit free text comments. Responses were analyzed in aggregate. Results: For ChatGPT-generated answers, the average quality ranged from 3.1 to 4.2 of 5.0, indicating they were generally “good” or “very good.” No answer received a unanimous 1-star (poor quality) or 5-star (excellent quality) score. Minor inaccuracies were found by at least 1 surgeon in 100% of the answers, and major inaccuracies were found in 36.6%. Regarding ChatGPT, 66.7% of surgeons thought it was an accurate source of information for patients. However, only 55.6% thought they were comparable with answers given by experienced thoracic surgeons, and only 44.4% would recommend it to their patients. Common criticisms of ChatGPT-generated answers included lengthiness, lack of specificity regarding surgical care, and lack of references. Conclusions: Chat-based artificial intelligence programs have potential to become a useful information tool for patients with lung cancer surgery. However, the quality and accuracy of ChatGPT-generated answers need improvement before thoracic surgeons would consider this method as a primary education source for patients.

Original languageEnglish
Pages (from-to)1174-1180.e18
JournalJournal of Thoracic and Cardiovascular Surgery
Volume169
Issue number4
DOIs
StatePublished - Apr 2025

Keywords

  • artificial intelligence
  • education
  • lung cancer
  • perioperative care

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