ArticleJournal of the American Medical Informatics Association : JAMIA2023
Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 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
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Impact of Digitalization on European General Practice From the Perspective of General Practitioners: Systematic Review.Journal of medical Internet research · 2026Pooled it
- The applications of ChatGPT and other large language models in anesthesiology and critical care: a systematic review.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Pooled it
- AI-Assisted Cardiovascular Risk Assessment by General Practitioners in Resource-Constrained Indonesian Settings Using a Conceptual Prototype: Randomized Controlled Study.Journal of medical Internet research · 2025Trial
- Drivers of Artificial Intelligence (AI) Adoption in Supporting Chronic Disease Management in Primary Care: A Scoping Review.Nursing reports (Pavia, Italy) · 2026Review
- Worldwide Innovative Network Consortium: Building a Common Global Cancer Database.JCO global oncology · 2026Review
- Whose Truth Is Ground Truth?: Consequences of Label Choice on Machine Learning Models Predicting Depression.Research square · 2026Article
- Artificial intelligence as a second reader for pneumothorax detection: redirecting rather than reducing chest CT use.BMC emergency medicine · 2026Article
- Pressure Injury Risk Assessment in Nursing Practice: A Head-to-Head Comparison of the Braden Scale and Machine Learning Models.Journal of clinical medicine · 2026Article
- The New Obesity Treatment Landscape: Challenges and Opportunities to Promote Shared Decision-Making in People With Obesity and Type 2 Diabetes.Diabetes care · 2026Review
- Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.BMJ paediatrics open · 2026Article
- New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026Review
- Using SCORE2 with a risk chart or online calculator: impact on model performance, treatment eligibility, and cardiovascular disease prevention.European heart journal. Quality of care & clinical outcomes · 2026Article
- Towards harmonization of Lyme diagnostics interpretation: external quality assessment using a web-based survey.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Patient Benefits in the Context of Sepsis-Related AI-Based Clinical Decision Support Systems: Scoping Review.Journal of medical Internet research · 2026Article
- Article
- Clinical problem selection for machine learning-based clinical decision support in the intensive care unit: complexity, actionability, and the way forward.Frontiers in medicine · 2026Article
- Evaluating the impact of AI assistance on decision-making in emergency doctors interpreting chest X-rays: a multi-reader multi-case study.Emergency medicine journal : EMJ · 2025Article
- A predictive model for evaluating the risk of latent tuberculosis relapse via machine learning.BMC infectious diseases · 2025Article
- Testing a Novel Design Framework for Patient-Facing Machine Learning-Based Predictions of Heart Failure Decompensation.JACC. Advances · 2025Article
- Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.NPJ digital medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveThis study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings. MATERIALS AND
methodsWe conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized.
resultsThirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n = 12) and risk assessment (n = 9). The majority of studies examined assistive CDS (n = 23) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed.
conclusionML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.
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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.