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Review
. 2022 Feb;15(2):309-321.
doi: 10.1111/cts.13175. Epub 2021 Oct 30.

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Elmer V Bernstam  1   2 , Paula K Shireman  3   4   5 , Funda Meric-Bernstam  6 , Meredith N Zozus  7 , Xiaoqian Jiang  1 , Bradley B Brimhall  4   8 , Ashley K Windham  4   8 , Susanne Schmidt  9 , Shyam Visweswaran  10 , Ye Ye  10 , Heath Goodrum  1 , Yaobin Ling  1 , Seemran Barapatre  10 , Michael J Becich  10
Affiliations
Review

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Elmer V Bernstam et al. Clin Transl Sci. 2022 Feb.

Abstract

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.

Keywords: artificial intelligence; machine learning; translational medical research.

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Conflict of interest statement

M.J.B. has a SFCOI in an AI in computational pathology company, SpIntellx, and is founder and stockholder. All other authors have declared no competing interests for this work.

Figures

FIGURE 1
FIGURE 1
A Artificial intelligence (AI) concepts
FIGURE 2
FIGURE 2
Artificial intelligence (AI) and machine learning (ML) project characteristics from Clinical and Translational Science Award (CTSA) institution survey (number of projects)
FIGURE 3
FIGURE 3
Disease categories of AI/ML Projects (Survey)
FIGURE 4
FIGURE 4
Disease categories of artificial intelligence/machine learning (AI/ML) Projects (Federally Funded Projects)
FIGURE 5
FIGURE 5
Computational domain of artificial intelligence/machine learning (AI/ML) Projects (Federally Funded Projects)
See this image and copyright information in PMC

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