Improvement of Patient Outcomes with AI

Rapid progress is being made in the study of artificial intelligence (AI) in healthcare, with promising applications already being shown in several areas of medicine. However, only a small number of these methods have been successfully implemented in actual clinical settings so far. This chapter delves into the primary difficulties and restrictions of AI in healthcare, as well as the measures needed to bring these potentially game-changing technologies out of the lab and into actual use. Dataset shift, accidental fitting of confounders, unexpected discriminating bias, the difficulties of generalization to new populations, and the unanticipated negative repercussions of new algorithms on health outcomes are all threats that developers of AI algorithms must keep an eye on. This chapter suggests that in the future, healthcare AI may use technology to analyze a patient’s health and issue an alert if it detects a decline. So that AI can aid in the treatment of specific disorders and take into account the patient’s perspective, researchers require large-scale patient-reported outcome databases.