Mark Day, EVP R&D, iRhythm:
Shift from retrospective to predictive analysis: In the near future, we expect AI innovation in healthcare to shift focus from retrospective analysis to predictive insight. To reach this milestone, health wearables must become both proficient and validated in determining who needs preventive care before symptoms and associated outcome risks manifest. At its core, digital health is meant to streamline complex processes and bring preventative care to high-risk populations. Predictive AI will help to deliver on this potential.
Bias in AI: Within the next year, AI companies will continue to improve data collection methods and develop processes that avoid bias in algorithm training and, in turn, performance in the intended population. Specifically, improved clinical study design will foster more heterogeneous and representative patient populations, resulting in algorithms that reduce bias. On the technical side, methods will develop to provide greater insight into the “black box” of AI algorithm decisions, which will guide understanding into whether these decisions represent bias based on factors including race, gender, and age.
This article was original published here.