The exponential growth of artificial intelligence (AI) and its much-touted form of machine learning (ML) has proven invaluable in many areas of patient care – from detection of malignant, hard-to-detect tumors in Barrett’s esophagus to cardiac monitoring through wearables to predicting the severity of COVID-19 of critically-ill patients in their ICUs.1-3 With COVID-19 accelerating the adoption of AI, constrained health systems and biomedical organizations are exploring ways that AI can improve operational, clinical, and research networks to provide better healthcare access, treatments and outcomes.
Most AI and ML enthusiasts will concede that while there is a huge promise and tremendous potential to deliver new solutions for possibly hundreds of much-needed patient interventions, there are still a lot of pitfalls to be considered. This is clearly evident in the lack of equity within healthcare systems, in areas such as ethics, data representation, and governance. These need to be sorted out as we scale AI across our world of healthcare. Here, we discuss the growth of healthcare AI and what leadership and executives need to consider to improve equity in the health system.