ArticlePloS one2019
Applications of artificial neural networks in health care organizational decision-making: A scoping review.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 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
121 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review.Current topics in medicinal chemistry · 2025Pooled it
- Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Briefings in bioinformatics · 2021Pooled it
- Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.Journal of medical systems · 2020Pooled it
- Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.Journal of pharmacokinetics and pharmacodynamics · 2020Pooled it
- Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia.American journal of physiology. Lung cellular and molecular physiology · 2023Trial
- Artificial intelligence for predicting 30-day mortality after emergency laparotomy.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2026Article
- Article
- Artificial Intelligence in the Assessment of Heart Rate Variability as an Instrument to Understand the Connection Between Psychologic and Psychiatric Conditions and the Heart.Bioengineering (Basel, Switzerland) · 2026Review
- Article
- Tracing Genetic Images Formed During Evolution.International journal of molecular sciences · 2026Article
- Modeling and optimization of methylene blue adsorption on biochar via artificial neural network (ANN).Biodegradation · 2026Article
- Factors associated with work-related musculoskeletal disorders using machine learning approaches: a systematic review.Annals of occupational and environmental medicine · 2026Review
- Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.Scientific reports · 2026Article
- MedicalPatchNet: a patch-based self-explainable AI architecture for chest X-ray classification.Scientific reports · 2026Article
- Reliable AI Platform for Monitoring BCI Caused Brain Injury and Providing Real-Time Protection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficiency of Kolmogorov-Arnold Networks in Small Medical Samples (Case Study of 2D Brain MRI Image Segmentation).Sovremennye tekhnologii v meditsine · 2026Article
- Development of interpretable machine learning models for predicting the probability of sepsis in patients with pulmonary fibrosis in the intensive care unit: based on MIMIC-IV and multi-database validation.Frontiers in cellular and infection microbiology · 2026Article
- An intuitionistic fuzzy automated negotiation model for personalized and efficient shared decision-making.Scientific reports · 2025Article
- Rapid thrombogenesis prediction in COVID-19 patients using DNN with data labeling.Scientific reports · 2025Article
- Advanced bioimpedance analysis for infectious disease risk assessment via neural network classifiers.Physical and engineering sciences in medicine · 2025Article
61 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997-2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.
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.