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Review
. 2020 Feb 1;155(2):148-158.
doi: 10.1001/jamasurg.2019.4917.

Artificial Intelligence and Surgical Decision-making

Tyler J Loftus  1 , Patrick J Tighe  2 , Amanda C Filiberto  1 , Philip A Efron  1 , Scott C Brakenridge  1 , Alicia M Mohr  1 , Parisa Rashidi  3 , Gilbert R Upchurch Jr  1 , Azra Bihorac  4
Affiliations
Review

Artificial Intelligence and Surgical Decision-making

Tyler J Loftus et al. JAMA Surg. .

Abstract

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.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Tighe reported grants from the National Institutes of Health during the conduct of the study. Dr Rashidi reported patents to Method and Apparatus for Pervasive Patient Monitoring pending and Systems and Methods for Providing an Acuity Score for Critically Ill or Injured Patients pending. Dr Bihorac reported grants from the National Institutes of Health and the National Science Foundation during the conduct of the study; in addition, Dr Bihorac has a patent to Systems and Methods for Providing an Acuity Score for Critically Ill or Injured Patients pending. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Surgical Decision-Making Paradigm
Figure 2.
Figure 2.. Optimal and Suboptimal Approaches to Surgical Decision-Making
Figure 3.
Figure 3.. Summary of Artificial Intelligence Techniques
AI indicates artificial intelligence; EHR, electronic health record.
See this image and copyright information in PMC

Comment in

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