Clarifying the elements of an algorithm--and their distinctive impact--will be increasingly important if machine learning is to overcome skepticism among healthcare stakeholders. Machine learning algorithms must offer insights that are credible and aligned with the scientific or clinical consensus. An algorithm that fails to replicate established findings or counters the established body of evidence is more likely an indication of a methodological oversight or a data artifact than a truly novel insight. A pharmaceutical manufacturer recently described a scenario in which a machine learning algorithm concluded that reducing low-density lipoprotein cholesterol after a heart attack was not associated with cardiac outcomes. This finding does not change 20-plus years of established clinical science, but rather speaks to nuances in the data and analytic structure. Without such a context, machine learning could conclude that cigarette lighters cause lung cancer. This context is provided by domain-specific expertise. Its absence results in decisions that, while analytically sound, produce algorithms that are not likely to be adopted. For instance, a recent machine learning algorithm to predict cardiovascular events included "lack of data" as a key risk factor.12

Artificial intelligence (AI) – the ability of computers to learn human-like functions or tasks – has shown great promise. What was previously considered the sole domain of human cognition is already being leveraged successfully across many industries. Now, the technology sector is witnessing what appears to be important new advances in AI that are bringing a new wave of interest for how it might shape the future of health and healthcare. The rapid digitization of health data through the use of heath information technology (health IT) in the United States has created major opportunities in the use of AI. Innovators and experts see potential in using digital health data to improve healthcare and health outcomes from the home to the clinic to the community.

UC San Francisco's Center for Digital Health Innovation (CDHI) today announced a collaboration with Intel Corporation to deploy and validate a deep learning analytics platform designed to improve care by helping clinicians make better treatment decisions, predict patient outcomes, and respond more nimbly in acute situations. The collaboration brings together Intel's leading edge computer science and deep learning capabilities with UCSF's clinical and research expertise to create a scalable, high-performance computational environment to support enhanced frontline clinical decision making for a wide variety of patient care scenarios. Until now, progress toward this goal has been difficult because complex, diverse datasets are managed in multiple, incompatible systems. This next-generation platform will allow UCSF to efficiently manage the huge volume and variety of data collected for clinical care as well as newer "big data" from genomic sequencing, monitors, sensors and wearables. These data will be integrated into a highly scalable "information commons" that will enable advanced analytics with machine learning and deep learning algorithms.