How deep learning influences workflows and roles in virtual surgical planning

Abstract

Abstract Background Deep learning (DL) has the potential to transform surgical practice, altering workflows and changing the roles of practitioners involved. However, studies have shown that introducing such change requires user acceptance. Following the development and presentation of a visual prototype for planning facial surgery interventions, the project aimed to understand the utility of DL, the implied workflow and role changes it would entail, and the potential barriers to its adoption in practice. Method This paper presents a multi-year case study providing insights from developing and introducing a visual prototype. The prototype was co-developed by facial surgeons, DL experts, and business process engineers. The study uses project data involving semi-structured interviews, workgroup results, and feedback from an external practitioner audience exposed to the prototype regarding their views on adopting DL tools in practice. Findings The surgeons attested a high utility to the application. However, the data also highlights a perceived need to remain in control, be able to intervene, and override surgical workflows in short intervals. Longer intervals without opportunities to intervene were seen with skepticism, suggesting that the practitioners’ acceptance of DL requires a carefully designed workflow in which humans can still take control of events. Conclusion Deep learning can improve and accelerate facial surgery intervention planning. Models from the business and management literature partially explain the acceptance of new technologies. Perceived ease of use seems less relevant than the perceived usefulness of new technology. Involving algorithms in clinical decision-making will change workflows and professional identities.

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