Publication date: Jan 03, 2019
Precision medicine has started to deliver positive improvements on health care delivery, patient care,
drug discovery, and preventive interventions. Notably, the incorporation of genomic, proteomic,
pharmacogenomic, molecular, and patient lifestyle data into clinical medicine promises to improve
our ability tailor medical treatment according to the individual characteristics of each patient.
Artificial intelligence (AI), or the use of computer algorithms to analyze, learn from, and predict
outcomes from medical data, has become an emerging tool to help physicians and scientists
translate big data to better patient outcomes and targeted care. However, despite these promising
opportunities for healthcare, AI also raises ethical, legal and policy challenges for all stakeholders of
the healthcare ecosystem. In particular, the prospect of using AI in our current Precision Medicine era
requires careful and continuous evaluation of how professionals, providers, organizations, and
industries integrate machine learning with clinical practice, data sharing, medical education, and
health systems. We explore several avenues where AI can advance medical diagnosis, performance,
and discovery as well as examine the major challenges that AI brings with its implementation in
science and health care.
Please signin to view all article content and metadata.