Home > Research > Publications & Outputs > Looking beyond the hype

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to journalJournal article

Published

Standard

Looking beyond the hype : Applied AI and machine learning in translational medicine. / Toh, T.S.; Dondelinger, F.; Wang, D.

In: EBioMedicine, Vol. 47, 01.09.2019, p. 607-615.

Research output: Contribution to journalJournal article

Harvard

APA

Vancouver

Author

Toh, T.S. ; Dondelinger, F. ; Wang, D. / Looking beyond the hype : Applied AI and machine learning in translational medicine. In: EBioMedicine. 2019 ; Vol. 47. pp. 607-615.

Bibtex

@article{4cad94c8ff944f31a697563338ac86ef,
title = "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 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",
author = "T.S. Toh and F. Dondelinger and D. Wang",
year = "2019",
month = sep
day = "1",
doi = "10.1016/j.ebiom.2019.08.027",
language = "English",
volume = "47",
pages = "607--615",
journal = "EBioMedicine",

}

RIS

TY - JOUR

T1 - Looking beyond the hype

T2 - Applied AI and machine learning in translational medicine

AU - Toh, T.S.

AU - Dondelinger, F.

AU - Wang, D.

PY - 2019/9/1

Y1 - 2019/9/1

N2 - 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.

AB - 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.

KW - Machine learning

KW - Drug discovery

KW - Imaging

KW - Genomic medicine

KW - Artificial intelligence

KW - Translational medicine

U2 - 10.1016/j.ebiom.2019.08.027

DO - 10.1016/j.ebiom.2019.08.027

M3 - Journal article

VL - 47

SP - 607

EP - 615

JO - EBioMedicine

JF - EBioMedicine

ER -