Bots in white coats: are large language models the future of patient education?: a multicenter cross-sectional analysis
Objectives: - Every year, around 300 million surgeries are conducted worldwide, with an estimated 4.2 million deaths occurring within 30 days after surgery. Adequate patient education is crucial, but often falls short due to the stress patients experience before surgery. Large language models (LLMs...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
March 2025
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| In: |
International journal of surgery
Year: 2025, Volume: 111, Issue: 3, Pages: 2376-2384 |
| ISSN: | 1743-9159 |
| DOI: | 10.1097/JS9.0000000000002250 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1097/JS9.0000000000002250 Verlag, kostenfrei, Volltext: https://journals.lww.com/international-journal-of-surgery/fulltext/2025/03000/bots_in_white_coats__are_large_language_models_the.5.aspx 
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| Author Notes: | Ughur Aghamaliyev, MD, Javad Karimbayli, PhD, Athanasios Zamparas, MD, Florian Bösch, MD, Michael Thomas, MD, Thomas Schmidt, MD, Christian Krautz, MD, Christoph Kahlert, MD, Sebastian Schölch, MD, Martin K. Angele, MD, Hanno Niess, MD, Markus O. Guba, MD, Jens Werner, MD, Matthias Ilmer, MD, Bernhard W. Renz, MD |
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| 520 | |a Objectives: - Every year, around 300 million surgeries are conducted worldwide, with an estimated 4.2 million deaths occurring within 30 days after surgery. Adequate patient education is crucial, but often falls short due to the stress patients experience before surgery. Large language models (LLMs) can significantly enhance this process by delivering thorough information and addressing patient concerns that might otherwise go unnoticed. - Material and methods: - This cross-sectional study evaluated Chat Generative Pretrained Transformer-4o’s audio-based responses to frequently asked questions (FAQs) regarding six general surgical procedures. Three experienced surgeons and two senior residents formulated seven general and three procedure-specific FAQs for both preoperative and postoperative situations, covering six surgical scenarios (major: pancreatic head resection, rectal resection, total gastrectomy; minor: cholecystectomy, Lichtenstein procedure, hemithyroidectomy). In total, 120 audio responses were generated, transcribed, and assessed by 11 surgeons from 6 different German university hospitals. - Results: - ChatGPT-4o demonstrated strong performance, achieving an average score of 4.12/5 for accuracy, 4.46/5 for relevance, and 0.22/5 for potential harm across 120 questions. Postoperative responses surpassed preoperative ones in both accuracy and relevance, while also exhibiting lower potential for harm. Additionally, responses related to minor surgeries were minimal, but significantly more accurate compared to those for major surgeries. - Conclusions: - This study underscores GPT-4o’s potential to enhance patient education both before and after surgery by delivering accurate and relevant responses to FAQs about various surgical procedures. Responses regarding the postoperative course proved to be more accurate and less harmful than those addressing preoperative ones. Although a few responses carried moderate risks, the overall performance was robust, indicating GPT-4o’s value in patient education. The study suggests the development of hospital-specific applications or the integration of GPT-4o into interactive robotic systems to provide patients with reliable, immediate answers, thereby improving patient satisfaction and informed decision-making. | ||
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