ReviewNPJ digital medicine2021
Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review.
Review in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 4 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
67 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies.BMC medical informatics and decision making · 2025Pooled it
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review.The oncologist · 2025Pooled it
- Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review.JAMA network open · 2022Pooled it
- Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial.Nature communications · 2025Trial
- Artificial Intelligence for Clinical Decision Support in Internal Medicine: A Systematic Review.Cureus · 2026Review
- Reproducibility of natural-language-driven statistical workflows compared with conventional software for diagnostic metrics in prostate cancer research.Investigative and clinical urology · 2026Article
- Five views on the foundations of data science.Patterns (New York, N.Y.) · 2026Article
- A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems.NPJ digital medicine · 2026Article
- A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.NPJ digital medicine · 2026Article
- Review
- Review
- Aligning Artificial Intelligence Prediction Targets with Clinical Workflows Using Human Centered Design Methods.medRxiv : the preprint server for health sciences · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Neuroscientific Framework of Cognitive-Behavioral Interventions for Mental Health Across Diverse Cultural Populations: A Systematic Review of Effectiveness, Delivery Methods, and Engagement.European journal of investigation in health, psychology and education · 2025Review
- Interpretable ensemble learning for tumor-type prediction with a SHAP-based evaluation of CatBoost and voting classifiers.Scientific reports · 2025Article
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- A qualitative Interview Study Investigating Patient, Health Professional, and Developer Perspectives on Real-World Implementation of Patient-Centered AI Systems.Research square · 2025Article
- Impact of Clinical Trial Virtualization on Recruitment in Underserved Communities for a Type 2 Diabetes mHealth Intervention.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
7 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evidence of the impact of traditional statistical (TS) and artificial intelligence (AI) tool interventions in clinical practice was limited. This study aimed to investigate the clinical impact and quality of randomized controlled trials (RCTs) involving interventions evaluating TS, machine learning (ML), and deep learning (DL) prediction tools. A systematic review on PubMed was conducted to identify RCTs involving TS/ML/DL tool interventions in the past decade. A total of 65 RCTs from 26,082 records were included. A majority of them had model development studies and generally good performance was achieved. The function of TS and ML tools in the RCTs mainly included assistive treatment decisions, assistive diagnosis, and risk stratification, but DL trials were only conducted for assistive diagnosis. Nearly two-fifths of the trial interventions showed no clinical benefit compared to standard care. Though DL and ML interventions achieved higher rates of positive results than TS in the RCTs, in trials with low risk of bias (17/65) the advantage of DL to TS was reduced while the advantage of ML to TS disappeared. The current applications of DL were not yet fully spread performed in medicine. It is predictable that DL will integrate more complex clinical problems than ML and TS tools in the future. Therefore, rigorous studies are required before the clinical application of these tools.
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.