ReviewNPJ digital medicine2019
Artificial intelligence and machine learning in clinical development: a translational perspective.
Review in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 172 papers, 5 of them syntheses 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
172 citing papers in PubMed, 5 syntheses or guidelines pooled it, 489 citations in OpenAlex.
- Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review.The oncologist · 2025Pooled it
- Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.JCO clinical cancer informatics · 2021Pooled it
- The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review.Journal of applied clinical medical physics · 2021Pooled it
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Benefits of Home-Based Solutions for Diagnosis and Treatment of Acute Coronary Syndromes on Health Care Costs: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Trial
- Review
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- Application of Artificial Intelligence and Machine Learning in Arrhythmia Detection and Pacemaker Data Analysis.Cardiology research · 2026Review
- Machine learning to improve the prediction of Large for Gestational Age (LGA) neonates: a cohort study.BMC pregnancy and childbirth · 2026Article
- Plasma proteomics improves thrombosis prediction in patients with cancer and identifies targetable IL-17-driven endothelial activation.Science translational medicine · 2026Article
- Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study.Communications medicine · 2026Article
- Evaluation of large language models for VI-RADS reports: a comparative analysis of zero-shot and few-shot prompting.BMC medical imaging · 2026Article
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- SPINK2 silencing suppresses leukemic proliferation and restores myeloid commitment via MECOM downregulation in acute myeloid leukaemia.Cell death discovery · 2026Article
- Review
- United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes.iScience · 2026Article
- Performance of an Intelligent Messaging Tool for Clinical Communications.JAMA network open · 2026Article
- Associations of blood cell indices with the risk of ankylosing spondylitis: A retrospective case-control and machine learning modeling study.SAGE open medicine · 2026Article
- Article
112 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.
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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.