ReviewSingapore medical journal2023
Machine learning in medicine: what clinicians should know.
Review in Singapore medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Pooled it
- Development and Validation of a Machine Learning-Based Model for Predicting All-Cause Mortality Risk in Patients With Type 2 Diabetes Mellitus Combined With Hypertension: National Cohort Study.JMIR medical informatics · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- From Molecules to Machines: An Integrative Framework Linking Molecular Pathogenesis, Multi-Factorial Risk, Risk Stratification, Clinical Management, and Artificial Intelligence in QT Prolongation and Sudden Cardiac Death.Clinical cardiology · 2026Review
- Impact of Query Language on the Structure and Guideline Alignment of AI-Generated Rehabilitation Programs in Chronic Kidney Disease.Journal of clinical medicine · 2026Article
- Artificial Intelligence in Sports Medicine: A Decision-Centered Framework for the Future Sports Physician.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Leveraging machine learning to identify optimal patient populations for PEG-IFN therapy in CHB and constructing nomogram models of interferon response: a 48-week follow-up study.BMC gastroenterology · 2026Article
- Multi-Omics Analysis of Morbid Obesity Using a Patented Unsupervised Machine Learning Platform: Genomic, Biochemical, and Glycan Insights.International journal of molecular sciences · 2026Article
- Artificial Intelligence in Medicine: Barriers, Solutions, and Strategies.HCA healthcare journal of medicine · 2026Article
- Regression-Based Analysis of Vestibular Laboratory Tests for the Prediction of Unilateral Vestibular Schwannoma.IEEE journal of translational engineering in health and medicine · 2026Article
- Current advancements and future directions in contrast-enhanced ultrasound (CEUS) in liver oncology: from quantification to theranostics.Polish journal of radiology · 2026Review
- Development and validation of a machine learning model for predicting hypersplenism in Wilson disease patients.Frontiers in medicine · 2026Article
- Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.Frontiers in big data · 2026Article
- Application of machine learning in the diagnostic work-up of telomere biology disorders.HemaSphere · 2026Article
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Establishment of a postoperative delirium risk prediction model for elderly hip fracture patients based on machine learning algorithms.BMC geriatrics · 2025Article
- Prognostic model and U-shaped SBP-risk correlation in unstable pelvic fractures with TBI.NPJ digital medicine · 2025Article
- Machine learning models for predicting renal injury in patients with gout.Renal failure · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.
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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.