Abstract
Machine learning and artificial intelligence (AI) in medicine has
arrived in medicine and the healthcare community is experiencing
significant growth in its adoption across numerous patient care
settings. There are countless applications for machine learning and AI
in medicine ranging from patient outcome prediction, to clinical
decision support, to predicting future patient therapeutic setpoints.
This commentary discusses a recent application leveraging machine
learning to predict one year patient survival following orthotopic heart
transplantation. This modeling approach has significant implications in
terms of improving clinical decision making, patient counseling, and
ultimately organ allocation and has been shown to significantly
outperform preexisting algorithms. This commentary also discusses how
adoption and advancement of this modeling approach in the future can
provide increased personalization of patient care. The continued
expansion of information systems and growth of electronic patient data
sources in healthcare will continue to pave the way for increased use
and adoption of data science in medicine. Personalized medicine has been
a long-standing goal of the healthcare community and with machine
learning and AI now being continually incorporated into clinical
settings and practice, this technology is well on the pathway to make a
considerable impact to greatly improve patient care in the near future.