Artikel ; Online: From Bytes to Best Practices: Tracing ChatGPT-3.5's Evolution and Alignment With the National Comprehensive Cancer Network® Guidelines in Pancreatic Adenocarcinoma Management.
2024 , Seite(n) 31348241248801
Abstract: Introduction: Artificial intelligence continues to play an increasingly important role in modern health care. ChatGPT-3.5 (OpenAI, San Francisco, CA) has gained attention for its potential impact in this domain.: Objective: To explore the role of ... ...
Abstract | Introduction: Artificial intelligence continues to play an increasingly important role in modern health care. ChatGPT-3.5 (OpenAI, San Francisco, CA) has gained attention for its potential impact in this domain. Objective: To explore the role of ChatGPT-3.5 in guiding clinical decision-making specifically in the context of pancreatic adenocarcinoma and to assess its growth over a period of time. Participants: We reviewed the National Comprehensive Cancer Network® (NCCN) Clinical Practice Guidelines for the Management of Pancreatic Adenocarcinoma and formulated a complex clinical question for each decision-making page. ChatGPT-3.5 was queried in a reproducible fashion. We scored answers on the following Likert scale: 5) Correct; 4) Correct, with missing information requiring clarification; 3) Correct, but unable to complete answer; 2) Partially incorrect; 1) Absolutely incorrect. We repeated this protocol at 3-months. Score frequencies were compared, and subgroup analysis was conducted on Results: In total, 50-pages of the NCCN Guidelines® were analyzed, generating 50 complex clinical questions. On subgroup analysis, the percentage of Conclusion: ChatGPT-3.5 represents an interesting but limited tool for assistance in clinical decision-making. We demonstrate that the platform evolved, and its responses to our standardized questions improved over a relatively short period (3-months). Future research is needed to determine the validity of this tool for this clinical application. |
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Sprache | Englisch |
Erscheinungsdatum | 2024-04-26 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ZDB-ID | 202465-2 |
ISSN | 1555-9823 ; 0003-1348 |
ISSN (online) | 1555-9823 |
ISSN | 0003-1348 |
DOI | 10.1177/00031348241248801 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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