BMJ health & care informatics · 2026
Barrier check study: why predictive machine learning struggles to reach the operating room.
СтатьяНазвание и аннотация: Перевод в очереди
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Ben Hmido Sara, Garita Chiara, Bloemers Frank, Rozie Sietske, Yeung Kak Khee, Kazemier Geert, Daams Freek
Аннотация
OBJECTIVES: Machine learning (ML) has the potential to enhance surgical decision-making through real-time risk prediction and personalised care yet clinical implementation remains limited. This study aimed to identify and categorise the key barriers to implementing ML tools in surgical practice.
METHODS: A nationwide qualitative survey was conducted among stakeholders involved in ML development or clinical implementation in healthcare. Participants provided open-ended responses describing perceived barriers, their impact and potential solutions. Responses were analysed using inductive thematic content analysis.
RESULTS: 95 participants from 53 organisations submitted 178 individual barrier entries. The most frequently reported barriers included limited clinician trust and artificial intelligence literacy, challenges integrating ML into surgical workflows, regulatory misalignment, insufficient data availability and quality, and unclear economic value. Barriers were highly interconnected, with challenges in one domain often reinforcing others. Proposed solutions emphasised interdisciplinary collaboration, early end-user involvement, improved transparency and training, privacy-preserving data-sharing approaches such as federated learning, and better alignment between regulatory, institutional and clinical structures.
CONCLUSION: The findings indicate that ML implementation in surgery is a socio-technical challenge rather than a purely technical one. Barriers often arise from misalignment between responsibility, decision authority and accountability across technical, clinical and organisational domains, particularly in time-critical surgical settings.
METHODS: A nationwide qualitative survey was conducted among stakeholders involved in ML development or clinical implementation in healthcare. Participants provided open-ended responses describing perceived barriers, their impact and potential solutions. Responses were analysed using inductive thematic content analysis.
RESULTS: 95 participants from 53 organisations submitted 178 individual barrier entries. The most frequently reported barriers included limited clinician trust and artificial intelligence literacy, challenges integrating ML into surgical workflows, regulatory misalignment, insufficient data availability and quality, and unclear economic value. Barriers were highly interconnected, with challenges in one domain often reinforcing others. Proposed solutions emphasised interdisciplinary collaboration, early end-user involvement, improved transparency and training, privacy-preserving data-sharing approaches such as federated learning, and better alignment between regulatory, institutional and clinical structures.
CONCLUSION: The findings indicate that ML implementation in surgery is a socio-technical challenge rather than a purely technical one. Barriers often arise from misalignment between responsibility, decision authority and accountability across technical, clinical and organisational domains, particularly in time-critical surgical settings.