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. 2024 Dec 9;13(1):2437330.
doi: 10.1080/28338073.2024.2437330. eCollection 2024.

Unravelling Orthopaedic Surgeons' Perceptions and Adoption of Generative AI Technologies

Matthias Schmidt  1 , Yasmin B Kafai  2 , Adrian Heinze  3 , Monica Ghidinelli  1
Affiliations

Unravelling Orthopaedic Surgeons' Perceptions and Adoption of Generative AI Technologies

Matthias Schmidt et al. J CME. .

Abstract

This mixed-methods study investigates the adoption of generative AI among orthopaedic surgeons, employing a Unified Theory of Acceptance and Use of Technology (UTAUT) based survey (n = 177) and follow-up interviews (n = 7). The research reveals varying levels of AI familiarity and usage patterns, with higher adoption in research and professional development compared to direct patient care. A significant generational divide in perceived ease of use highlights the need for tailored training approaches. Qualitative insights uncover barriers to adoption, including the need for more evidence-based support, as well as concerns about maintaining critical thinking skills. The study exposes a complex interplay of individual, technological, and organisational factors influencing AI adoption in orthopaedic surgery. The findings underscore the need for a nuanced approach to AI integration that considers the unique aspects of orthopaedic surgery and the diverse perspectives of surgeons at different career stages. This provides valuable insights for educational institutions and healthcare organisations in navigating the challenges and opportunities of AI adoption in specialised medical fields.

Keywords: Generative AI; UTAUT; continuing medical education; human-AI collaboration; orthopedic surgery; technology adoption.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Familiarity with AI technologies. Self-reported familiarity with different AI technologies (7-point likert scale: 1 – not familiar at all, 7 – extremely familiar), n = 177.
Figure 2.
Figure 2.
Conceptions of generative AI. Thematic analysis of an open-ended question asking respondents to define generative AI in their own words. N = 129.
Figure 3.
Figure 3.
Self-reported usage frequency of generative AI. How frequent do you use generative AI tools in the following aspects of your clinical workflow? N = 177.
Figure 4.
Figure 4.
Distribution of effort expectancy across age groups. Comparative analysis of the effort expectancy averaging three statements: “I believe that generative AI is easy to use”, “learning to operate generative AI will be easy for me”, and “interacting with generative AI will be clear and understandable” (7-point likert scale: 1 – strongly disagree to 7 – strongly agree). N = 177. P-values show the significant pairwise comparisons with tukey-hsd.
Figure 5.
Figure 5.
Applications of generative AI in practice. Thematic analysis of interview transcripts. N = 7.
Figure 6.
Figure 6.
Barriers of generative AI adoption. Thematic analysis of interview transcripts. N = 7.
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