SynthesisBMC medical imaging2025
A systematic literature review: exploring the challenges of ensemble model for medical imaging.
Synthesis in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of artificial intelligence-based deep learning models in detecting furcation involvement: A systematic review and meta-analysis.Journal of periodontology · 2026Pooled it
- Diagnostic Accuracy of Deep Learning Models in Detecting Peri-Implant Marginal Bone Loss: A Systematic Review and Meta-Analysis.Clinical oral implants research · 2026Pooled it
- A Clinician-Configurable FAHP-Based Ensemble Framework for Multi-Label Chest X-Ray Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- Automated landmark detection on lateral photographs using artificial intelligence: diagnostic accuracy compared to expert annotations.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026Article
- Article
- Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.Frontiers in medicine · 2026Article
- MedFuse: dual-stream fusion of convolutional and vision transformer-based features for enhanced medical image classification.Frontiers in medicine · 2026Article
- Category-Aware Two-Stage Divide-and-Ensemble Framework for Sperm Morphology Classification.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical imaging has been essential and has provided clinicians with useful information about the human body to diagnose various health issues. Early diagnosis of diseases based on medical imaging can mitigate the risk of severe consequences and enhance long-term health outcomes. Nevertheless, the task of diagnosing diseases based on medical imaging can be challenging due to the exclusive ability of clinicians to interpret the outcomes of medical imaging, which is time-consuming and susceptible to human fallibility. The ensemble model has the potential to enhance the accuracy of diagnoses of diseases based on medical imaging by analyzing vast volumes of data and identifying trends that may not be immediately apparent to doctors. However, it takes a lot of memory and processing resources to train and maintain several ensemble models. These challenges highlight the necessity of effective and scalable ensemble models that can manage the intricacies of medical imaging assignments.
methodsThis study employed an SLR technique to explore the latest advancements and approaches. By conducting a thorough and systematic search of Scopus and Web of Science databases in accordance with the principles outlined in the PRISMA, employing keywords namely ensemble model and medical imaging.
resultsThis study included a total of 75 papers that were published between 2019 and 2024. The categorization, methodologies, and use of medical imaging were key factors examined in the analysis of the 30 cited papers included in this study, with a focus on diagnosing diseases.
conclusionsResearchers have observed the emergence of an ensemble model for disease diagnosis using medical imaging since it has demonstrated improved accuracy and may guide future studies by highlighting the limitations of the ensemble model.
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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.