JMIR Publications

ABSTRACT

Artificial intelligence (AI), the computerized capability of doing tasks which until recently have been thought the exclusive domain of human intelligence, has demonstrated great strides in the past decade. The ability to play games, provide piloting for an automobile, and respond to spoken language are remarkable successes. How are the challenges and opportunities of medicine different from these challenges and how can we best apply these data-driven techniques to patient care and outcomes? Scott Blois, in a New England Journal of Medicine paper published in 1980, suggested that more well-defined “specialized” tasks of medical care were more amenable to computer assistance, while the breadth of approach required for defining a problem and narrowing down the problem space was less so, and perhaps unachievable. On the other hand, one can argue that the modern version of AI, which uses data-driven approaches, will be the most useful in tackling tasks such as outcome prediction that are often difficult for clinicians and patients. The ability today to collect large volumes of data about a single individual (e.g., through a wearable device) and the accumulation of large datasets about multiple persons receiving medical care has the potential to apply to the care of individuals. As these techniques of analysis, enumeration, aggregation and presentation are brought to bear in medicine, the question arises as to their utility and applicability in that domain. Early efforts in decision support were found to be helpful; later experiences, as the systems proliferated, have shown difficulties arising with alert fatigue and physician burnout becoming more prevalent. Will something similar arise with data-driven predictions? Will empowering patients by equipping them with information gained from data analysis help? Patient, provider, technology, and policymakers each have a role to play when it comes to the development and utilization of AI in medicine. Some of the challenges, opportunities and tradeoffs implicit here are presented as a dialog between a clinician (SJN) and an informatician (QZ).