| Literature DB >> 31277839 |
Coryandar Gilvary1, Neel Madhukar2, Jamal Elkhader3, Olivier Elemento4.
Abstract
Stakeholders 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.Keywords: artificial intelligence; drug development; machine learning; model interpretability
Mesh:
Year: 2019 PMID: 31277839 DOI: 10.1016/j.tips.2019.06.001
Source DB: PubMed Journal: Trends Pharmacol Sci ISSN: 0165-6147 Impact factor: 14.819