• Coryandar Gilvary, Neel Madhukar, Jamal Elkhader, and Olivier Elemento.
• HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA; OneThree Biotech, New York, NY 10021, USA.
• Trends Pharmacol. Sci. 2019 Aug 1; 40 (8): 555-564.

AbstractStakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can improve efficiency and decrease costs. In this Opinion, we discuss some of the key factors that should be prioritized to enable the successful integration of AI across the healthcare value chain. In particular, we believe a focus on model interpretability is crucial to obtain a deeper understanding of the underlying biological mechanisms and guide further investigations. Additionally, we discuss the importance of integrating diverse types of data within any AI framework to limit bias, increase accuracy, and model the interdisciplinary nature of medicine. We believe that widespread adoption of these practices will help accelerate the continued integration of AI into our current healthcare framework.Copyright © 2019 Elsevier Ltd. All rights reserved.

15 keywords...

hide…