A Comparison of ChatGPT and Multidisciplinary Team Meeting Treatment Recommendations in 10 Consecutive Cervical Cancer Patients.

Autor:	Ebner F; Department of Obstetrics and Gynecology, Alb-Donau Klinikum (ADK), Ehingen, DEU., Hartkopf A; Department of Obstetrics and Gynecology, University of Tübingen, Tübingen, DEU., Veselinovic K; Department of Obstetrics and Gynecology, University of Ulm, Ulm, DEU., Schochter F; Department of Obstetrics and Gynecology, University of Ulm, Ulm, DEU., Janni W; Department of Obstetrics and Gynecology, University Hospital of Ulm, Ulm, DEU., Lukac S; Department of Obstetrics and Gynecology, University of Ulm, Ulm, DEU., Dayan D; Department of Obstetrics and Gynecology, University Hospital of Ulm, Ulm, DEU.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2024 Aug 22; Vol. 16 (8), pp. e67458. Date of Electronic Publication: 2024 Aug 22 (Print Publication: 2024).
DOI:	10.7759/cureus.67458
Abstrakt:	Background The preparation of multidisciplinary team (MDT) meetings can be time-consuming. In addition to the clinical data being available digitally in subsystems, the preparation of more complex cases requires literature research. Several expert systems have been developed to support this process. However, the interaction with these systems has to be trained. Current development enables linguistic interaction with such artificial intelligence (AI) systems. To the best of our knowledge, these have not been tested as premedical screening tools for MDT. Methods This is a retrospective consecutive case series of 10 cervical cancer cases comparing the medical recommendations of the MDT and artificial intelligence (AI) on a low level (i.e., surgery, systemic treatment, and radiotherapy). Results The clinical cases ranged from primary diagnosis via suspected recurrence to palliative settings. The AI repeatedly stated that medical professionals need to be consulted before treatment decisions. The AI answers ranged from no agreement to overachievement by mentioning treatment options for preexisting risk factors (such as obesity). In standard cases, the AI answer matched well with the expert recommendations. In some cases, the AI answers were contrary to our treatment recommendation. Conclusion The interaction with current language AIs is temptingly easy, and the replies are very understandable. Despite the AI warning regarding medical recommendations in the majority of our cases, there was a good match with the MDT recommendations. However, in some cases, the medical evidence behind the answers was missing or in the worst case fictional. In our case series, the AI did not meet the requirements to support a clinical MDT meeting by prescreening the therapeutic options. However, it did exceed the expectations regarding the risk factors of the patients. Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. (Copyright © 2024, Ebner et al.)
Databáze:	MEDLINE
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