Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.
artificial intelligence
machine learning
translational medical research
Journal
Clinical and translational science
ISSN: 1752-8062
Titre abrégé: Clin Transl Sci
Pays: United States
ID NLM: 101474067
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
received:
10
08
2021
accepted:
01
10
2021
pubmed:
28
10
2021
medline:
26
2
2022
entrez:
27
10
2021
Statut:
ppublish
Résumé
Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011-2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.
Identifiants
pubmed: 34706145
doi: 10.1111/cts.13175
pmc: PMC8841416
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
309-321Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR000371
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001857
Pays : United States
Organisme : NIH HHS
ID : U01 TR002393
Pays : United States
Organisme : NLM NIH HHS
ID : K99 LM013383
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM011829
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG044271
Pays : United States
Organisme : NCATS NIH HHS
ID : U01 TR002393
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002645
Pays : United States
Organisme : NCATS NIH HHS
ID : U24 TR002260
Pays : United States
Informations de copyright
© 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.
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