ReviewClinical and translational science2022
Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.
Review in Clinical and translational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Applications in Medical Devices for Personalized Health Care Solutions: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence revolutionizing CNS drug discovery and development.Drug discovery today · 2026Review
- Mapping the Digital Mind: A Meta-Analysis of EEG Biomarkers in Cognition, Emotion, and Mental Health.Brain sciences · 2026Review
- Towards accurate artificial intelligence models for strain-level phage-host prediction.Briefings in bioinformatics · 2026Article
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- Article
- Navigating the bench to bedside maze: Lessons from CTSA hubs on streamlining research support in academic medical centers.Journal of clinical and translational science · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review.International journal of oral science · 2023Review
- Artificial Intelligence and Public Health: An Exploratory Study.International journal of environmental research and public health · 2023Article
- WGS Data Collections: How Do Genomic Databases Transform Medicine?International journal of molecular sciences · 2023Review
- Prevaccination Prevalence of Type-Specific Human Papillomavirus Infection by Grade of Cervical Cytology in Estonia.JAMA network open · 2023Article
- SERS-Based Biosensors Combined with Machine Learning for Medical Application.ChemistryOpen · 2023Review
- Utilizing the Un-Meeting model to advance innovative translational and team science.Journal of clinical and translational science · 2023Article
- Artificial Intelligence in Public Health: Current Trends and Future Possibilities.International journal of environmental research and public health · 2022Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.