Artificial Intelligence and Surgical Decision-Making

By T. Loftus, Patrick James Tighe, Amanda C. Filiberto, P. Efron, S. Brakenridge, A. Mohr, Parisa Rashidi, G. Upchurch, Azra Bihorac. JAMA Surgery, 2019

465 citations recorded by the source corpus.

IMPORTANCE Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making. OBSERVATIONS Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process. CONCLUSIONS AND RELEVANCE Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.

View Paper: https://www.semanticscholar.org/paper/383dc22d1d6ca1310626ec56a63cd578273c2f93

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Citations in the corpus

Expanding Explainability: Towards Social Transparency in AI systems (2021, background): “being employed in high stakes decision-making scenarios in industries such as healthcare [63], finance [76], college admissions [79], hiring [19], and criminal justice [37], the need for explainability becomes paramount.”