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When Predictive Models Collide.

No trend is more visible in medicine than integrating machine learning and artificial intelligence (AI) into clinical care. Terms like “random forests” and “convolutional neural networks” that were previously the domain of computer science PhDs are now part of the vocabulary of both C-suite administrators and frontline clinicians. As the high-performance storage and computing power that underlie these models become a commodity, both the supply and demand for predictive models based on machine learning will only increase. Advances in genomics and patient-generated health data will enrich predictive models, but will also increase model complexity even as they increase precision. How can clinicians andmodelers prevent themessy accidents that could happen when these models collide?