Artificial intelligence in simulation-based training for Health Professions Education: Navigating the rabbit hole
Abstract
Simulation-based training (SBT) for health professions education has seen an evolution from low-fidelity trainers to technology-integrated high-fidelity trainers, which has opened doors to newer and promising prospects of integration of artificial intelligence (AI) into SBT. This review provides insights into the use of AI to augment and transform various elements of SBT like complex scenario designing, realism, feedback, student engagement, etc. This is exemplified through the successful application of AI in various SBT platforms, which have increased the efficacy of simulations. However, several challenges and barriers have been perceived in the use of AI in SBT, which include bias in AI algorithms originating due to skewed training datasets leading to inaccurate decisions, errors due to black box, cost factors, need for constant update, and ethical, legal, and cultural issues. Despite these challenges, the railroads of AI are fast-tracking with increased interest and collaborative ventures between various stakeholders like healthcare professionals, educators, technological experts, and policymakers. This article attempts to provide a comprehensive overview of the role of AI in SBT, challenges, and the way forward to amalgamating it with SBT in an optimal manner.
How strong is the evidence?
Evidence Hub reads this as a review, done in animal, not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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