Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Physicians' acceptance of large language model–based clinical decision support tools in gynecologic oncology: a technology acceptance model study. |
| Συγγραφείς: |
Stalp, Jan Lennart, Schneider, Juliane Alexandra, Krause, Anna, Steinkasserer, Lena, Hachenberg, Jens, Denecke, Agnieszka, Hillemanns, Peter, Wolff, Dominik |
| Πηγή: |
Frontiers in Digital Health; 2026, p1-8, 8p |
| Θεματικοί όροι: |
Psychology of physicians, Generative artificial intelligence, Statistical correlation, Data security, Risk assessment, Fear, Professional autonomy, Academic medical centers, Data analysis, Labor productivity, Medical errors, Research funding, Clinical decision support systems, Pilot projects, Questionnaires, Physicians' attitudes, Oncology, Descriptive statistics, Decision making in clinical medicine, Uncertainty, Gynecology, Surveys, Motivation (Psychology), Intention, Statistics, Research, Social skills, Communication, Clinics, Data analysis software, Needs assessment, Regression analysis, Hope, Time |
| Γεωγραφικοί όροι: |
Germany |
| Περίληψη: |
Introduction: Medical decision-making is characterized by rapidly evolving evidence being represented by national and international guidelines that form the decision basis for treatment recommendations. Large language model-based clinical decision support systems (LLM-CDSS) have shown promising potential to serve as a support tool, e.g., in gynecologic oncology. Nevertheless, physicians' acceptance of this technology in gynecologic oncology is currently not backed by evidence. Therefore, we present a pilot study evaluating the clinical technology acceptance of LLM-CDSS in gynecologic oncology. Methods: We designed a questionnaire based on the existing technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) characteristics, which was sent to clinicians employed at a clinic of gynecology and obstetrics of a large tertiary hospital. A total of 29 physicians spanning the complete range of clinical experience answered the survey. For identifying factors influencing the willingness to use a LLM-CDSS, Ordinary Least Squares (OLS) regression was applied based on answers grouped by question categories. Further, descriptive analysis was performed by measures of central tendency and correlation. Results: OLS regression did not identify any question categories significantly influencing willingness to use LLM-CDSS. Answer distribution was left skewed for the primary endpoint, indicating an acquiescence bias in the survey population. Still, descriptive findings indicate a generally positive but cautious attitude toward LLM-based therapy recommendation systems in gynecologic oncology. Acceptance appeared to depend strongly on transparency, integration of evidence, clinical validation, and the preservation of physician oversight and accountability. Discussion: The participants agreed on a possible positive influence of LLM-CDSS for clinical practice. However, the results emphasize that such tools should support rather than replace physicians to leave the final medical decision to the human. Participants were divided on whether patients must be informed about the usage of such tools whereas some participants reported concerns about their clinical autonomy and patients' trust. The survey needs to be validated in a larger multicentered cohort in a shortened version. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |