SynthesisTranslational vision science & technology2020
Introduction to Machine Learning, Neural Networks, and Deep Learning.
Synthesis in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 551 papers, 14 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
551 citing papers in PubMed, 14 syntheses or guidelines pooled it, 984 citations in OpenAlex.
- Machine learning prediction models for intravenous immunoglobulin resistance in Kawasaki disease: a meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Machine learning-based risk predictive model for postoperative refractures in patients with osteoporotic vertebral compression fractures: a systematic review and critical appraisal.BMC musculoskeletal disorders · 2026Pooled it
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Risk prediction models for postmenopausal osteoporosis: a systematic review and meta-analysis study.BMC musculoskeletal disorders · 2026Pooled it
- Deep learning models for pancreatic cancer detection on CT: a meta-analysis.Frontiers in medicine · 2026Pooled it
- Predictive Value of Machine Learning in Knee Osteoarthritis Progression: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Relevance of Prognosis Tools in Periodontitis: A Systematic Review.Oral health & preventive dentistry · 2025Pooled it
- The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.The veterinary quarterly · 2026Review
- Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD.Renal failure · 2026Article
- Predicting nonunion - a hybrid machine-learning model combining patient factors and mechanobiological simulation features exceeds clinician accuracy.Bone & joint research · 2026Article
- The Role of Artificial Intelligence in Optimizing Diagnosis in Prostate Cancer-A Narrative Review.Journal of clinical medicine · 2026Review
- Machine learning-based screening tool for predicting the risk of oropharyngeal dysphagia in patients with ischemic stroke.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Artificial Intelligence for Esophageal Precancerous Lesions and Esophageal Cancer.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
491 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
5 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods: A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology. Results: A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background. Conclusions: Artificial intelligence has a promising future in medicine; however, many challenges remain. Translational Relevance: The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.
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.