Wide application of artificial intelligence (AI) in health care could greatly improve patient safety, reduce physician burnout, and improve the overall efficiency of health care from scheduling and billing to making surgeries more safe. But the health care industry lags in AI adoption due to inherent technical challenges and because the financial benefits of AI adoption are often difficult to measure, according to a new review published in The New England Journal of Medicine.

The study authors detailed the AI challenges confronting health care compared with other industries.

“Early AI took root in business sectors in which large amounts of structured, quantitative data were available and the computer algorithms, which are the heart of AI, could be trained on discrete outcomes—for example, a customer looked at a product and bought it or did not buy it,” they wrote. “Qualitative information, such as clinical notes and patients’ reports, are generally harder to interpret, and multifactorial outcomes associated with clinical decision-making make algorithm training more difficult.”