SynthesisJMIR medical informatics2025
Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.
Synthesis in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis 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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- The role and utility of artificial intelligence and machine learning for diagnostic prediction in general practice.The European journal of general practice · 2026Article
- Drivers of Artificial Intelligence (AI) Adoption in Supporting Chronic Disease Management in Primary Care: A Scoping Review.Nursing reports (Pavia, Italy) · 2026Review
- Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability.JMIR medical informatics · 2026Article
- Interpretable machine learning to predict NOAF in ICU patients with CKD: validation in US and Chinese cohorts.Frontiers in medicine · 2026Article
- AI in Prostate Cancer Screening & Diagnosis: A Registry-Based Study of ClinicalTrials.gov Trials.Inquiry : a journal of medical care organization, provision and financingArticle
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI)-based diagnostic prediction models could aid primary care (PC) in decision-making for faster and more accurate diagnoses. AI has the potential to transform electronic health records (EHRs) data into valuable diagnostic prediction models. Different prediction models based on EHR have been developed. However, there are currently no systematic reviews that evaluate AI-based diagnostic prediction models for PC using EHR data. Objective: This study aims to evaluate the content of diagnostic prediction models based on AI and EHRs in PC, including risk of bias and applicability. Methods: This systematic review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE, Embase, Web of Science, and Cochrane were searched. We included observational and intervention studies using AI and PC EHRs and developing or testing a diagnostic prediction model for health conditions. Two independent reviewers (LH and AC) used a standardized data extraction form. Risk of bias and applicability were assessed using PROBAST (Prediction Model Risk of Bias Assessment Tool). Results: From 10,657 retrieved records, a total of 15 papers were selected. Most EHR papers focused on 1 chronic health care condition (n=11, 73%). From the 15 papers, 13 (87%) described a study that developed a diagnostic prediction model and 2 (13%) described a study that externally validated and tested the model in a PC setting. Studies used a variety of AI techniques. The predictors used to develop the model were all registered in the EHR. We found no papers with a low risk of bias, and high risk of bias was found in 9 (60%) papers. Biases covered an unjustified small sample size, not excluding predictors from the outcome definition, and the inappropriate evaluation of the performance measures. The risk of bias was unclear in 6 papers, as no information was provided on the handling of missing data and no results were reported from the multivariate analysis. Applicability was unclear in 10 (67%) papers, mainly due to lack of clarity in reporting the time interval between outcomes and predictors. Conclusions: Most AI-based diagnostic prediction models based on EHR data in PC focused on 1 chronic condition. Only 2 papers tested the model in a PC setting. The lack of sufficiently described methods led to a high risk of bias. Our findings highlight that the currently available diagnostic prediction models are not yet ready for clinical implementation in PC.
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.