ReviewDigital health
Current applications and future challenges of machine learning and artificial intelligence in clinical trials: A scoping review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Long-term enoxaparin use and osteoporosis risk: a real-world cohort study with integrative computational and network toxicology approaches.Journal of translational medicine · 2026Article
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Machine learning (ML) and artificial intelligence (AI) applications have increased across different stages of clinical research. Their use in clinical trials (CTs) has been discussed but not quantified. Methods: A scoping review was conducted by searching PubMed, Embase (Ovid), and Scopus for CTs or protocols. The goal was to understand the extent of ML and AI applications in the design, conduct, and analysis of CTs. Screening was performed on Covidence, with GPT model support. Findings: After title/abstract and full-text screening, 108 records were included; in some studies, AI/ML was applied across multiple stages. For the design, 20 studies involved advanced methods, six applied them to stratification, four to treatment selection during randomization, six to participant selection, two for outcome assessment, and two for site selection. Seven studies involved them in the collection and analysis of data from wearable devices, and one for monitoring. More commonly, AI/ML has been used at the analysis stage of 93 CTs; however, limitations in reporting trial objectives make it difficult to distinguish the purpose between primary and exploratory analyses. Interpretation: This research identifies a serious mismatch between the potential and actual applications of ML in CTs. Considering the potential benefits of ML in CTs, such underuse could hinder the evolution of CTs toward faster and more efficient approaches.
Indexed as
Identifiers
What Socratic 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 Socratic graph.