Looking beyond the hype: Applied AI and machine learning in translational medicine
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 mach...
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doaj-2d40e2dfd28b4398a58c0a37b72f779c2020-11-25T02:01:36ZengElsevierEBioMedicine2352-39642019-09-0147607615Looking beyond the hype: Applied AI and machine learning in translational medicineTzen S. Toh0Frank Dondelinger1Dennis Wang2The Medical School, University of Sheffield, Sheffield, UKLancaster Medical School, Furness College, Lancaster University, Bailrigg, Lancaster, UKNIHR Sheffield Biomedical Research Centre, University of Sheffield, Sheffield, UK; Department of Computer Science, University of Sheffield, Sheffield, UK; Corresponding author at: NIHR Sheffield BRC, University of Sheffield, 385a Glossop Road, Sheffield S10 2HQ, UK.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 medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale. Keywords: Machine learning, Drug discovery, Imaging, Genomic medicine, Artificial intelligence, Translational medicinehttp://www.sciencedirect.com/science/article/pii/S2352396419305493 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tzen S. Toh Frank Dondelinger Dennis Wang |
spellingShingle |
Tzen S. Toh Frank Dondelinger Dennis Wang Looking beyond the hype: Applied AI and machine learning in translational medicine EBioMedicine |
author_facet |
Tzen S. Toh Frank Dondelinger Dennis Wang |
author_sort |
Tzen S. Toh |
title |
Looking beyond the hype: Applied AI and machine learning in translational medicine |
title_short |
Looking beyond the hype: Applied AI and machine learning in translational medicine |
title_full |
Looking beyond the hype: Applied AI and machine learning in translational medicine |
title_fullStr |
Looking beyond the hype: Applied AI and machine learning in translational medicine |
title_full_unstemmed |
Looking beyond the hype: Applied AI and machine learning in translational medicine |
title_sort |
looking beyond the hype: applied ai and machine learning in translational medicine |
publisher |
Elsevier |
series |
EBioMedicine |
issn |
2352-3964 |
publishDate |
2019-09-01 |
description |
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 medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale. Keywords: Machine learning, Drug discovery, Imaging, Genomic medicine, Artificial intelligence, Translational medicine |
url |
http://www.sciencedirect.com/science/article/pii/S2352396419305493 |
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