Jul 2026· Journal of Clinical Technology and Theory· Vol 4, pp. 51-56· 0 citations
TL;DR
This narrative review summarizes applications of supervised learning, unsupervised learning, and deep learning in clinical diagnosis, prognosis prediction, patient stratification, and biomarker discovery and finds that machine learning is useful when the clinical question is clear, data quality is acceptable, and validation is strict.
Abstract
Clinical biostatistics has traditionally relied on regression-based models to explain associations, estimate risks, and support medical decision-making. The growth of electronic health records, imaging data, omics data, and follow-up data has made clinical information larger, less tidy, and more difficult to model with only classical methods. Machine learning is increasingly used in this setting because it can capture non-linear patterns and handle high-dimensional predictors. This narrative review summarizes applications of supervised learning, unsupervised learning, and deep learning in clinical diagnosis, prognosis prediction, patient stratification, and biomarker discovery. Literature was selected from PubMed, Web of Science, and Google Scholar, with emphasis on studies and reporting guidelines published from 2019 to 2025. This review finds that machine learning is useful when the clinical question is clear, data quality is acceptable, and validation is strict. However, a stronger algorithm does not automatically become a better clinical tool. Main limitations include weak interpretability, biased training data, poor transportability, overreliance on Area Under the Curve (AUC), and incomplete reporting. Machine learning should therefore be treated as a complement to traditional biostatistics rather than a simple replacement.
In recent years, interest in machine learning applications has grown rapidly, particularly in the medical domain, where large amounts of data are available for training these models. This review focuses on the potential of machine learning for early diagnosis and prognosis of chronic kidney disease (CKD) by examining the most recent literature. Articles published from 2016 to 2025 were collected from online databases such as PubMed, Web of Science, and Embase. After abstract and full-text screening, 57 articles were included in the results section. Machine learning was applied to clinical and laboratory data, medical imaging, urine samples, retinal images, and at-home measurements to diagnose CKD and predict CKD progression and related complications. Although many studies reported high discriminatory performance, the evidence base was dominated by retrospective, single-center, and methodologically heterogeneous studies, with frequent high-risk-of-bias findings and limited external validation. Furthermore, most published models are not yet sufficiently validated for clinical deployment. Before these tools can be adopted in routine care, prospective, multicenter studies are required that report calibration and clinical utility, adhere to established reporting standards, and demonstrate added value over the current standard of care.
Leon Van de Putte, M. Speeckaert· Diagnostics· 0 citations
Real-world data from electronic health records, registries, claims, molecular testing and routine imaging have become central to contemporary oncology because they describe populations, treatments and outcomes that are incompletely represented in conventional trials. Machine learning can extend the utility of these data by extracting phenotypes from unstructured records, modelling prognosis, integrating clinicogenomic information and prioritising patients for clinical action. Yet the same combination creates a compound validity problem: routine-care data are generated by clinical processes rather than experimental design, while machine-learning models can amplify measurement error, confounding, selection effects and distribution shift. This critical narrative review evaluates the evidence linking real-world evidence and machine learning to predictive oncology and clinical decision support. Literature published from 1 January 2015 to 30 June 2026 was examined, with emphasis on peer-reviewed oncology studies, methodological guidance and prospective evaluations. The evidence is strongest for scalable extraction of treatment response, progression, performance status and mortality-related phenotypes, and for prognostic risk stratification using structured and narrative electronic health-record data. Evidence that prediction improves care is more limited. Randomised oncology studies show that machine-learning-triggered behavioural interventions can increase serious-illness conversations and reduce some forms of end-of-life treatment, but prospective evidence for treatment recommendation systems and large language model-enabled support remains dominated by concordance, simulation and workflow outcomes rather than patient benefit. Across the literature, external validation, calibration, outcome validity, transportability and separation of prognostic from causal questions are recurrent weaknesses. Real-world evidence can therefore be a powerful substrate and evaluation environment for predictive oncology, but data scale cannot substitute for fit-for-purpose measurement or causal design. Clinical translation should proceed through transparent reporting, independent validation, prospective workflow evaluation, equity assessment and continuous post-deployment monitoring, with human oversight retained for decisions in which model errors carry substantial clinical consequences.
Unknown authors· Archives of Current Research...· 0 citations
This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Kanishk Yadav, Taneesha Gupta· Journal of the Egyptian Nati...· 0 citations
INTRODUCTION
Cardiovascular (CV) adverse events are increasingly recognized in patients with cancer. Previous reviews of AI/ML have focused on single cancer types, imaging-based data, and lacked evaluation of methodological rigor. This systematic review synthesized AI/ML models developed to predict CV adverse events from patient-level clinical data across diverse cancer populations.
METHODS
This review followed the PRISMA 2020 guidelines. PubMed and Web of Science were searched through 26 October 2025. Study characteristics, model development, and handling of features and missing data were extracted. Study quality was assessed using the IJMEDI checklist.
RESULTS
Of 32 included studies, 18 compared multiple algorithms and 14 used a single algorithm. Random forest and XGBoost were the most common methods (n = 17, respectively), and XGBoost was most often the best-performing model in multi-algorithm studies, although substantial study heterogeneity precludes concluding general algorithmic superiority. Common limitations were unreported missing data handling (n = 17), limited external validation (n = 8), and rare calibration assessment (n = 4). Most studies were rated medium quality (n = 28).
CONCLUSIONS
AI/ML models show promise for predicting CV adverse events in patients with cancer; however, clinical applicability is constrained by insufficient preprocessing transparency, limited external validation, and inadequate calibration reporting.
Li-Wei Wu, Minh-Anh Le-Dang, B. Okoye et al.· Expert review of pharmacoeco...· 0 citations
Risk prediction tools are becoming increasingly popular tools to assist in making clinical decisions. The models, however, are typically trained on data from general patient cohorts and may not be representative of and applicable to targeted patient cohorts when used in practice. This study overcame these obstacles by developing and evaluating a clinical risk prediction model using the MIMIC-III clinical dataset and an Artificial Neural Network (ANN). The suggested method accounts for all possible preprocessing steps—including normalization, encoding, managing missing values, and data balancing using SMOTE-ENN—to increase the dependability of the predictions. With an F1-Score (F1) of 96.9%, an accuracy (ACC) of 98.6%), a precision (PRE) of 97%, and a recall (REC) of 96.5%, the ANN model has high predictive capacity and can capture the complicated interaction between the clinical variables. The outcomes demonstrate that the suggested ANN architecture can accurately and consistently assess clinical risk, which in turn allows for the early identification of high-risk patients and aids healthcare providers in making data-informed therapeutic decisions. Because it can provide trustworthy prediction models from complex health data, the proposed method has great potential for clinical real-time application. It can also help develop smarter healthcare decision-support systems and enhance patient monitoring methods.
Shivani Jain· Journal of Artificial Intell...· 0 citations
The proposed GPT2-based table-to-text framework provides a practical and clinically interpretable approach for disease prediction from limited structured healthcare data and demonstrates strong potential for early risk detection, transparent clinical decision support, and reliable deployment in real-world low-resource healthcare environments.
S. Bin Akter, S. Akter, D. Eisenberg et al.· medRxiv· 0 citations
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