Challenges and opportunities for artificial intelligence in surgery

Pamela Andreatta, Christopher S. Smith, John Christopher Graybill, Mark Bowyer and Eric Elster

The Journal of Defense Modeling and Simulation, 2022, vol. 19, issue 2, 219-227

Abstract: Surgery is an exceptionally complex domain where multi-dimensional expertise is developed over an extended period of time, and mastery is maintained only through ongoing engagement in surgical contexts. Expert surgeons integrate perceptual information through both conscious and subconscious awareness, and respond to the environment by leveraging their deep understanding of surgical constructs. However, their ability to utilize these deep knowledge structures can be complicated by continuous advances in technology, medical science, pharmacology, technique, materials, operative environments, etc. that must be routinely accommodated in professional practice. The demands on surgeons to perform perfectly in ever-changing contexts increases cognitive load, which could be reduced through judicious use of accurate and reliable artificial intelligence (AI) systems. AI has great potential to support human performance in complex environments such as surgery; however, the foundational requirements for the rules governing algorithmic development of performance requirements necessitate the active involvement of surgeons to precisely model the quantitative measures of performance along the continuum of expertise. Providing the AI development community with these data will help assure that accurate and reliable systems are designed to supplement human performance in applied surgical contexts. The Military Health System’s Clinical Readiness Program is developing these types of metrics to support military medical readiness.

Keywords: Artificial intelligence; surgical education; surgical training; surgery; professional development; performance support systems; maintenance of competency; competency maintenance; surgical simulation; AI performance support; just-in-time training; surgical modeling; modeling and simulation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:joudef:v:19:y:2022:i:2:p:219-227

DOI: 10.1177/15485129211022855

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