ReviewAging clinical and experimental research2023
A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.
Review in Aging clinical and experimental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 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
49 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in orthopaedic education, training and research: a systematic review.BMC medical education · 2025Pooled it
- Artificial Intelligence and Decision-Making in Oncology: A Review of Ethical, Legal, and Informed Consent Challenges.Current oncology reports · 2025Pooled it
- Social Media and eHealth Literacy Among Older Adults: Systematic Literature Review.Journal of medical Internet research · 2025Pooled it
- Technical Approaches to Predicting Acute Deterioration in Pediatric Inpatients: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Clin-STAR Corner: Practice Changing Advances at the Interface of Artificial Intelligence/Machine Learning and Geriatrics.Journal of the American Geriatrics Society · 2026Review
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 2026Review
- A shape optimization method for resistance reduction of local piping components with multiscale validation.Communications engineering · 2026Article
- Utilising unsupervised machine learning to predict outbreaks of respiratory tract infections in acute Irish hospitals (2016-2021).Public health in practice (Oxford, England) · 2026Article
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.Journal of clinical medicine · 2026Review
- Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review.Diagnostic and prognostic research · 2026Review
- Machine Learning-Based Prediction of Institutional Delivery Dropout (IDD) Among Nigerian Women: An Exploratory Study Using SHAP Interpretability.Journal of epidemiology and global health · 2026Article
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Semi-Supervised Clustering for Identification of MCI and Dementia Cohorts with a Brief Digital Cognitive Assessment.Research square · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- The oral microbiome as a regulatory hub for systemic health: a systematic review of mechanistic links and clinical implications.Journal of oral microbiology · 2026Review
- Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.PloS one · 2026Article
- Review
- Artificial Intelligence in the Management of Infectious Diseases in Older Adults: Diagnostic, Prognostic, and Therapeutic Applications.Biomedicines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
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.