ReviewEuropean journal of medical research2025
Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.
Review in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence-based detection of neuropsychiatric lupus: an exploratory meta-analysis of neuroimaging and multimodal biomarker models.Clinical and experimental medicine · 2026Pooled it
- A meta-analytic approach to identification of clinical biomarkers of hearing loss in cognitive impairment.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.International dental journal · 2026Review
- SHAP-Based Feature Augmentation and Stacking Ensemble Learning for ECG-Based Serum Potassium Abnormality Prediction.Bioengineering (Basel, Switzerland) · 2026Article
- WDCN: a comprehensive neural network based approach for estimating breast cancer risk.Briefings in bioinformatics · 2026Article
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Development and Validation of Non-Invasive Machine-Learning Screening Models for Pediatric Malnutrition in Hospitalized Children: A Single-Center Study.Children (Basel, Switzerland) · 2026Article
- Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.BMC medical imaging · 2026Article
- Review
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- Predicting infected pancreatic necrosis in acute pancreatitis using machine learning models and feature selection.Scientific reports · 2026Article
- Machine learning prediction model for early postoperative hypoalbuminemia after pulmonary surgery: a retrospective case-matched comparative study.Journal of thoracic disease · 2026Article
- Enhancing diabetes risk stratification through natural language processing: a multimodal data integration approach.Frontiers in public health · 2026Article
- Multi‑feature Prediction Model for Coronary Heart Disease Comorbidity in Middle‑aged and Older Adults with COPD Based on Machine Learning and SHAP.International journal of chronic obstructive pulmonary disease · 2026Article
- Harnessing AI-driven approaches for detecting metabolic dysfunction-associated steatotic liver disease, assessing fibrosis, and stratifying hepatocellular carcinoma risk: a scoping review.Frontiers in oncology · 2026Review
- Editorial: Harnessing machine learning for enhanced biomedical diagnosis and early disease detection: bridging data science and healthcare.Frontiers in oncology · 2026Article
- Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.PloS one · 2026Article
- Machine Learning Application in Enhancing HIV Management and Treatment Outcomes: Revolutionizing HIV Infection.Current HIV research · 2026Review
- Future Research Directions on Mycobacterium tuberculosis Proteins.Advances in experimental medicine and biology · 2026Review
- The Relationship Between Urinary Neonicotinoid Concentrations and Obesity Among Individuals Aged 35 to 74 in Guangxi, China.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of Machine Learning (ML) and Deep Learning (DL) technologies has revolutionized healthcare, particularly in the domains of disease prediction and diagnosis. This study provides a comprehensive review of ML and DL applications across sixteen diverse diseases, synthesizing findings from research conducted between 2015 and 2024. We explore these technologies' methodologies, effectiveness, and clinical outcomes, highlighting their transformative potential in healthcare settings. Although ML and DL demonstrate remarkable accuracy and efficiency in disease prediction and diagnosis, challenges including quality of data, interpretability of models, and their integration into clinical workflows remain significant barriers. By evaluating advanced approaches and their outcomes, this review not only underscores the current capabilities of ML and DL but also identifies key areas for future research. Ultimately, this work aims to serve as a roadmap for advancing healthcare practices, enhancing clinical decision making, and strengthening patient outcomes through the effective and responsible implementation of AI-driven technologies.
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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.