TY - JOUR
T1 - Artificial intelligence in clinical and translational science
T2 - Successes, challenges and opportunities
AU - Bernstam, Elmer V.
AU - Shireman, Paula K.
AU - Meric-Bernstam, Funda
AU - N. Zozus, Meredith
AU - Jiang, Xiaoqian
AU - Brimhall, Bradley B.
AU - Windham, Ashley K.
AU - Schmidt, Susanne
AU - Visweswaran, Shyam
AU - Ye, Ye
AU - Goodrum, Heath
AU - Ling, Yaobin
AU - Barapatre, Seemran
AU - Becich, Michael J.
N1 - Funding Information:
This study was supported in part by National Center for Advancing Translational Sciences (NCATS) Synergy paper program through the Center for Leading Innovation and Collaboration (CLIC), NCATS grants UL1 TR000371 (Center for Clinical and Translational Sciences), UL1 TR001857, UL1 TR002645, NCATS and Office of the Director, NIH U01 TR002393, National Library of Medicine grants R01 LM011829 and K99 LM013383, National Institute of Aging P30 AG044271, PCORI CDRN‐1306‐04608, the Reynolds and Reynolds Professorship in Clinical Informatics, and the Cancer Prevention Research Institute of Texas (CPRIT) Data Science and Informatics Core for Cancer Research (RP170668).
Funding Information:
This study was supported in part by National Center for Advancing Translational Sciences (NCATS) Synergy paper program through the Center for Leading Innovation and Collaboration (CLIC), NCATS grants UL1 TR000371 (Center for Clinical and Translational Sciences), UL1 TR001857, UL1 TR002645, NCATS and Office of the Director, NIH U01 TR002393, National Library of Medicine grants R01 LM011829 and K99 LM013383, National Institute of Aging P30 AG044271, PCORI CDRN-1306-04608, the Reynolds and Reynolds Professorship in Clinical Informatics, and the Cancer Prevention Research Institute of Texas (CPRIT) Data Science and Informatics Core for Cancer Research (RP170668). The authors thank Travis Holder at the Houston Academy of Medicine?Texas Medical Center library for help with the literature review as well as Dr. Jonathan Silverstein for helpful comments on earlier drafts of this paper.
Funding Information:
Motivated by the desire to facilitate clinically relevant applications of AI, we present three complementary “views” onto this rapidly evolving field. First, we conducted a scoping review of biomedical AI efforts in the published literature. We adopted the National Center for Advancing Translational Science (NCATS) vision of translational science, to identify challenges and opportunities for AI across the translational science spectrum. However, the published literature provides an incomplete view. For example, applications implemented and maintained by the clinical enterprise may not be described in publications. Thus, we surveyed CTSA hubs to self‐identify existing, funded AI projects at CTSA hubs. Finally, to identify additional projects and ongoing work, we analyzed biomedical AI projects at CTSA hubs funded by the US National Institutes of Health (NIH) and the US National Science Foundation (NSF). 10 11
Publisher Copyright:
© 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - machine learning
KW - translational medical research
UR - http://www.scopus.com/inward/record.url?scp=85118257321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118257321&partnerID=8YFLogxK
U2 - 10.1111/cts.13175
DO - 10.1111/cts.13175
M3 - Review article
C2 - 34706145
AN - SCOPUS:85118257321
SN - 1752-8054
VL - 15
SP - 309
EP - 321
JO - Clinical and translational science
JF - Clinical and translational science
IS - 2
ER -