Looking beyond the hype: Applied AI and machine learning in translational medicine.

ABSTRACT: Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medici

SUBMITTER: Toh TS 

PROVIDER: S-EPMC6796516 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature