In Reply We thank Kar et al for their thoughtful appraisal of artificial intelligence–based decision-support systems and delays between technological advances and clinical application in reference to our article.1 Similar phenomena have been observed for pharmaceutical innovations and evidence-based guidelines, with lag times commonly exceeding 10 years.2 It seems prudent to address these delays in context, promoting early adoption of low-risk, high-benefit interventions supported by level I evidence while exercising caution for high-risk interventions supported by weak evidence. We agree with Kar et al that evidence supporting artificial intelligence augmentation of clinical decision-making is currently weak and discuss 2 potential clinical hazards in a JAMA Surgery article1: the use of biased or misrepresentative model training data could produce erroneous model outputs, and many patients could be harmed in a short time frame if model outputs are not carefully monitored and interpreted. Kar et al wisely propose that large, robust data sets are needed to allow self-learning, minimize the effect of outliers in small data, and ensure model validity and generalizability. We echo these sentiments and remain hopeful that application of the Fast Healthcare Interoperability Resources framework to large-scale, multi-institutional electronic health record data will achieve these important objectives. This would require cooperation and collaboration among clinicians, data scientists, computer engineers, administrators, implementation scientists, and most importantly patients, who deserve data privacy and security as well as personalized, precise, shared decision-making.
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Loftus TJ, Upchurch GR, Bihorac A. Reactive Artificial Intelligence Using Big Data in the Era of Precision Medicine—Reply. JAMA Surg. 2020;155(7):671–672. doi:10.1001/jamasurg.2020.0856
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