Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery

Autor: Marco‐Christopher Rupp, Lukas B. Moser, Silvan Hess, Peter Angele, Matthias Aurich, Felix Dyrna, Stefan Nehrer, Markus Neubauer, Johannes Pawelczyk, Kaywan Izadpanah, Johannes Zellner, Philipp Niemeyer, AGA‐Komitee Innovation und Translation
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Experimental Orthopaedics, Vol 11, Iss 3, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 2197-1153
DOI: 10.1002/jeo2.12080
Popis: Abstract Purpose The purpose of this study was to evaluate the perspective of orthopaedic surgeons on the impact of artificial intelligence (AI) and to evaluate the influence of experience, workplace setting and familiarity with digital solutions on views on AI. Methods Orthopaedic surgeons of the AGA Society for Arthroscopy and Joint Surgery were invited to participate in an online, cross‐sectional survey designed to gather information on professional background, subjective AI knowledge, opinion on the future impact of AI, openness towards different applications of AI, and perceived advantages and disadvantages of AI. Subgroup analyses were performed to examine the influence of experience, workplace setting and openness towards digital solutions on perspectives towards AI. Results Overall, 360 orthopaedic surgeons participated. The majority indicated average (43.6%) or rudimentary (38.1%) AI knowledge. Most (54.5%) expected AI to substantially influence orthopaedics within 5–10 years, predominantly as a complementary tool (91.1%). Preoperative planning (83.8%) was identified as the most likely clinical use case. A lack of consensus was observed regarding acceptable error levels. Time savings in preoperative planning (62.5%) and improved documentation (81%) were identified as notable advantages while declining skills of the next generation (64.5%) were rated as the most substantial drawback. There were significant differences in subjective AI knowledge depending on participants' experience (p = 0.021) and familiarity with digital solutions (p
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