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Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

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The Effect of Riding as an Alternative Treatment for Children with Cerebral Palsy: A Systematic Review and Meta-Analysis

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Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating

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PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

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Calculating the sample size required for developing a clinical prediction model

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A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

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Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study

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A Preoperative Nomogram for Disease Recurrence Following Radical Prostatectomy for Prostate Cancer

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Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations

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Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

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Prediction models for cardiovascular disease risk in the general population: systematic review

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Reporting of artificial intelligence prediction models

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Reporting and Methods in Clinical Prediction Research: A Systematic Review

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No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

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Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis

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Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

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Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

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Deep learning-based survival prediction of oral cancer patients

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Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis

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Sample-size determination methodologies for machine learning in medical imaging research: a systematic review

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Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

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Prognostic models for breast cancer: a systematic review

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Reporting methods in studies developing prognostic models in cancer: a review

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A systematic review of machine learning models for predicting outcomes of stroke with structured data

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A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods

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Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example

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Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model

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Risk Prediction Models for Colorectal Cancer: A Systematic Review

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Survival outcome prediction in cervical cancer: Cox models vs deep-learning model

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Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation.

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A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes

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Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study

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Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.

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Predicting Early Death Among Elderly Dialysis Patients: Development and Validation of a Risk Score to Assist Shared Decision Making for Dialysis Initiation

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Predicting Breast Cancer Recurrence Using Machine Learning Techniques

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Stability Investigations of Multivariable Regression Models Derived from Low- and High-Dimensional Data

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Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery

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Predicting breast cancer 5-year survival using machine learning: A systematic review

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Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study

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Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies

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Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma

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Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool

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Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

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Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies

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Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy

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Predicting radiation pneumonitis in locally advanced stage II–III non-small cell lung cancer using machine learning

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Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysis

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Extent of Resection in Meningioma: Predictive Factors and Clinical Implications

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A risk score for predicting perioperative blood transfusion in liver surgery

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Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance

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Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review

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Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

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Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance

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Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods

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Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

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A Validated Clinical Risk Prediction Model for Lung Cancer in Smokers of All Ages and Exposure Types: A HUNT Study

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The Nottingham Prognostic Index: five- and ten-year data for all-cause Survival within a Screened Population

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Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine

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askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men

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A Predictive Model for Postembolization Syndrome after Transarterial Hepatic Chemoembolization of Hepatocellular Carcinoma

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Use of the Gail Model and Breast Cancer Preventive Therapy Among Three Primary Care Specialties

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A novel prediction method for lymph node involvement in endometrial cancer: machine learning

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Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data

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Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters

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Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study

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Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data

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Artificial neural network models to predict nodal status in clinically node-negative breast cancer

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Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data

Yu-Hsiang, W., Phung-Anh, N., Mohaimenul, I. M., Li, Y., & Yang, H. (2019) · Studies in health technology and informatics · doi:10.3233/shti190259 · Cited by 41 · see citing works

Nomograms for predicting the overall and cause-specific survival in patients with malignant peripheral nerve sheath tumor: a population-based study

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Predicting Survival of Patients with Spinal Ependymoma Using Machine Learning Algorithms with the SEER Database

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Creating Prognostic Systems for Well-Differentiated Thyroid Cancer Using Machine Learning

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Accuracy Enhanced Lung Cancer Prognosis for Improving Patient Survivability Using Proposed Gaussian Classifier System

Kaviarasi, R., & Gandhi Raj, R. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1297-2 · Cited by 39 · see citing works

Semi-supervised learning to improve generalizability of risk prediction models

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Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

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A systematic review of methodology used in the development of prediction models for future asthma exacerbation

Bridge, J., Blakey, J., & Bonnett, L. J. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-0913-7 · Cited by 37 · see citing works

Patient‐based prediction algorithm of relapse after allo‐HSCT for acute Leukemia and its usefulness in the decision‐making process using a machine learning approach

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Sample sizes of prediction model studies in prostate cancer were rarely justified and often insufficient

Collins, S. D., Peek, N. B., Riley, R. D., & Martin, G. P. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.12.011 · Cited by 31 · see citing works

A machine-learning–based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies

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A prospective study examining cachexia predictors in patients with incurable cancer

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Response to repeat echoendoscopic celiac plexus neurolysis in pancreatic cancer patients: A machine learning approach

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Age and Lymphovascular Invasion Accurately Predict Sentinel Lymph Node Metastasis in T2 Melanoma Patients

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Predicting cervical cancer screening among sexual minority women using Classification and Regression Tree analysis

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Survivability prediction of colon cancer patients using neural networks

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Gait speed and survival of older surgical patient with cancer: Prediction after machine learning

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Posterior fossa meningiomas: perioperative predictors of extent of resection, overall survival and progression-free survival

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Clinical characteristics and disease-specific prognostic nomogram for primary gliosarcoma: a SEER population-based analysis

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Can machine learning predict resecability of a peritoneal carcinomatosis?

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Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach

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Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review

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A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer

Bradley, A., Meer, R. V. D., & McKay, C. J. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027192 · Cited by 19 · see citing works

A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning

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Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck

Tighe, D. F., Lewis-Morris, T., & Freitas, A. A. (2019) · British Journal of Oral and Maxillofacial Surgery · doi:10.1016/j.bjoms.2019.05.026 · Cited by 16 · see citing works

Development of a Novel Prognostic Risk Score for Predicting Complications of Penectomy in the Surgical Management of Penile Cancer

Velázquez, N., Press, B. H., Renson, A., Wysock, J. S., Taneja, S. S., Huang, W. C. W., & Bjurlin, M. A. (2018) · Clinical Genitourinary Cancer · doi:10.1016/j.clgc.2018.09.018 · Cited by 15 · see citing works

Predictive model algorithms identifying early and advanced stage ER+/HER2− breast cancer in claims data

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Prediction of 10-year Overall Survival in Patients with Operable Cervical Cancer using a Probabilistic Neural Network

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Risk Factors for Local Relapse and Inferior Disease-free Survival After Breast-conserving Management of Breast Cancer: Recursive Partitioning Analysis of 2161 Patients

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Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy

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Recursive Partitioning Analysis (RPA) of Prognostic Factors for Overall Survival in Patients with Spinal Metastasis: A New System for Stratified Treatment

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Predictors of the therapeutic effect of corticosteroids on radiation-induced optic neuropathy following nasopharyngeal carcinoma

Zheng, B., Lin, J., Li, Y., Zhuo, X., Huang, X., Shen, Q., & Tang, Y. (2019) · Supportive Care in Cancer · doi:10.1007/s00520-019-04699-z · Cited by 7 · see citing works

Deep stacked sparse auto-encoders for prediction of post-operative survival expectancy in thoracic lung cancer surgery

Iraji, M. S. (2019) · Journal of Applied Biomedicine · doi:10.32725/jab.2018.007 · Cited by 7 · see citing works

Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms

Thapa, S., Fischbach, L. A., Delongchamp, R. R., Faramawi, M. F., & Orloff, M. S. (2019) · Gastroenterology Research and Practice · doi:10.1155/2019/8321942 · Cited by 7 · see citing works

A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer

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Early Warning Models to Estimate the 30-Day Mortality Risk After Stent Placement for Patients with Malignant Biliary Obstruction

Zhou, H.-F., Lü, J., Zhu, H., Guo, J., Huang, M. X., Ji, J., Lv, W., Li, Y., Xu, H., Chen, L., et al. (2019) · CardioVascular and Interventional Radiology · doi:10.1007/s00270-019-02331-5 · Cited by 5 · see citing works

Predicting Disease-Free Lung Cancer Survival Using Patient Reported Outcome (PRO) Measurements with Comparisons of Five Machine Learning Techniques (MLT)

Sim, J.-A., & Yun, Y. H. (2019) · Studies in health technology and informatics · doi:10.3233/shti190548 · Cited by 5 · see citing works

A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer

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Developing case‐finding algorithms for second events of oropharyngeal cancer using administrative data: A population‐based validation study

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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718 · see citing works

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

(2024) · BMJ · doi:10.1136/bmj.q902 · Cited by 416 · see citing works

Stability of clinical prediction models developed using statistical or machine learning methods

Riley, R. D., & Collins, G. S. (2023) · Biometrical Journal · doi:10.1002/bimj.202200302 · Cited by 211 · see citing works

APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support

Kwong, J. C. C., Khondker, A., Lajkosz, K., McDermott, M. B. A., Borrat, X., McCradden, M. D., Mamdani, M., Kulkarni, G. S., & Johnson, A. E. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.35377 · Cited by 151 · see citing works

Evidence of questionable research practices in clinical prediction models

White, N. M., Parsons, R., Collins, P. G. S., & Barnett, A. G. (2023) · BMC Medicine · doi:10.1186/s12916-023-03048-6 · Cited by 149 · see citing works

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Andaur Navarro, C. L., Damen, J. A. A., van Smeden, M., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., et al. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.11.015 · Cited by 121 · see citing works

Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112 · see citing works

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

Dhiman, P., Ma, J., Qi, C., Bullock, G. S., Sergeant, J. C., Riley, R. D., & Collins, P. G. S. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02008-1 · Cited by 99 · see citing works

Importance of sample size on the quality and utility of AI-based prediction models for healthcare

Riley, R. D., Ensor, J., Snell, K. I. E., Archer, L., Whittle, R., Dhiman, P., Alderman, J. E., Liu, X., Kirton, L., Manson-Whitton, J., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.01.013 · Cited by 93 · see citing works

Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide

López‐Ayala, P., Riley, R. D., Collins, P. G. S., & Zimmermann, T. (2025) · BMJ · doi:10.1136/bmj-2024-082440 · Cited by 88 · see citing works

Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis

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Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review

Ma, J., Dhiman, P., Qi, C., Bullock, G. S., van Smeden, M., Riley, R. D., & Collins, P. G. S. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.07.017 · Cited by 78 · see citing works

The Improved Kidney Risk Score in ANCA-Associated Vasculitis for Clinical Practice and Trials

Bate, S. G., McGovern, D., Costigliolo, F., Tan, P. G., Krátký, V., Scott, J., Chapman, G. B., Brown, N., Floyd, L., Brilland, B., et al. (2023) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000274 · Cited by 78 · see citing works

Artificial intelligence in ovarian cancer histopathology: a systematic review

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Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches

Riley, R. D., Collins, P. G. S., Kirton, L., Snell, K. I. E., Ensor, J., Whittle, R., Dhiman, P., van Smeden, M., Liu, X., Alderman, J. E., et al. (2025) · BMJ · doi:10.1136/bmj-2024-080749 · Cited by 54 · see citing works

AI in imaging: the regulatory landscape

Hill, D. L. G. (2024) · British Journal of Radiology · doi:10.1093/bjr/tqae002 · Cited by 52 · see citing works

Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review

dos Santos, C. F., & Amorim‐Lopes, M. (2025) · BMC Medical Research Methodology · doi:10.1186/s12874-025-02463-y · Cited by 59 · see citing works

Digital twins in oncology: From predictive modelling to personalised treatment strategies

Olawade, D. B., Oisakede, E. O., Bello, O. J., Analikwu, C. C., Egbon, E., & Ojo, A. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105171 · Cited by 47 · see citing works

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.03.012 · Cited by 41 · see citing works

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review

Tsegaye, B., Snell, K. I. E., Archer, L., Kirtley, S., Riley, R. D., Sperrin, M., CALSTER, B. V., Collins, P. G. S., & Dhiman, P. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.111675 · Cited by 36 · see citing works

Development and international validation of logistic regression and machine‐learning models for the prediction of 10‐year molar loss

Troiano, G., Nibali, L., Petsos, H., Eickholz, P., Saleh, M. H. A., Santamaria, P., Jiao, J., Shi, S., Meng, H., Zhurakivska, K., et al. (2022) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.13739 · Cited by 35 · see citing works

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Kom, I. Y., Dongelmans, D. A., de Keizer, N. F., Jager, K. J., Schut, M. C., Abu–Hanna, A., & Klopotowska, J. E. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad014 · Cited by 34 · see citing works

Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI

Kwong, J. C. C., Wu, J., Malik, S., Khondker, A., Gupta, N. K., Bodnariuc, N., Narayana, K., Malik, M., van der Kwast, T., Johnson, A. E. W., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01088-7 · Cited by 35 · see citing works

Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology

Kastrup, N., Holst‐Kristensen, A. W., & Valentin, J. B. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00088-7 · Cited by 34 · see citing works

PMFN-SSL: Self-supervised learning-based progressive multimodal fusion network for cancer diagnosis and prognosis

Li, L. K., Pan, H., Liang, Y., Shao, M., Xie, S., Lu, S., & Liao, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111502 · Cited by 30 · see citing works

Enhanced lithology identification with few-shot well-logging data using a Confidence-Enhanced Semi-Supervised Meta-Learning Approach

Li, H., Sun, Y., & Qiao, S. (2025) · Measurement · doi:10.1016/j.measurement.2025.116762 · Cited by 29 · see citing works

Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis

Zaka, A., Mutahar, D., Gorcilov, J., Gupta, A. K., Kovoor, J. G., Stretton, B., Mridha, N., Sivagangabalan, G., Thiagalingam, A., Chow, C. K., et al. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae074 · Cited by 28 · see citing works

Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes

Zhong, J., Liu, X., Lu, J. A., Yang, J., Zhang, G., Mao, S., Chen, H., Yin, Q., Cen, Q., Jiang, R., et al. (2025) · European Radiology · doi:10.1007/s00330-024-11331-0 · Cited by 28 · see citing works

An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity

Akano, T. T., & James, C. C. (2022) · Beni-Suef University Journal of Basic and Applied Sciences · doi:10.1186/s43088-022-00327-8 · Cited by 27 · see citing works

Twelve practical recommendations for developing and applying clinical predictive models

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Clinical prediction models using machine learning in oncology: challenges and recommendations

Collins, G. S., Chester-Jones, M., Gerry, S., Ma, J., Matos, J., Sehjal, J., Tsegaye, B., & Dhiman, P. (2025) · BMJ Oncology · doi:10.1136/bmjonc-2025-000914 · Cited by 27 · see citing works

Performance of the Palliative Prognostic Index for cancer patients: A systematic review and meta-analysis

Yoong, S. Q., Porock, D. C., Whitty, D., Tam, W., & Zhang, H. (2023) · Palliative Medicine · doi:10.1177/02692163231180657 · Cited by 22 · see citing works

Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis

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Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study

Kwong, J. C. C., Khondker, A., Meng, E., Taylor, N., Kuk, C., Perlis, N., Kulkarni, G. S., Hamilton, R. J., Fleshner, N. E., Finelli, A., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00067-5 · Cited by 21 · see citing works

Application of machine learning in tax prediction: A review with practical approaches

Akinrinola, O., Addy, W. A., Ajayi-Nifise, A. O., Odeyemi, O., & Falaiye, T. O. (2024) · Global Journal of Engineering and Technology Advances · doi:10.30574/gjeta.2024.18.2.0028 · Cited by 19 · see citing works

Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality

Krepper, D., Cesari, M., Hubel, N. J., Zelger, P., & Sztankay, M. J. (2024) · Journal of Patient-Reported Outcomes · doi:10.1186/s41687-024-00808-7 · Cited by 19 · see citing works

Integration of Federated Learning and Blockchain in Healthcare: A TutorialPreprint

Shahsavari, Y., Dambri, O. A., Baseri, Y., Hafid, A. S., & Makrakis, D. (2024) · doi:10.36227/techrxiv.171340632.25905526/v1 · Cited by 18 · see citing works

A systematic review of machine learning applications in predicting opioid associated adverse events

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Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction

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Diagnostic test accuracy of externally validated convolutional neural network (CNN) artificial intelligence (AI) models for emergency head CT scans – A systematic review

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Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance

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ContraSurv: Enhancing Prognostic Assessment of Medical Images via Data-Efficient Weakly Supervised Contrastive Learning

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Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders Treatment Outcomes

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The Actual Performance of ML/AI Models in Predicting Radiation-Induced Toxicity in Head and Neck Cancer: A Systematic Review and Meta-Analysis.

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Development and internal validation of a prediction model for long-term opioid use—an analysis of insurance claims data

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Evaluation of changes in prediction modelling in biomedicine using systematic reviews

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Artificial Intelligence in Cancer Clinical Research: II. Development and Validation of Clinical Prediction Models

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Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review

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Planned but ever published? A retrospective analysis of clinical prediction model studies registered on clinicaltrials.gov since 2000

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The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews

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Prediction model protocols indicate better adherence to recommended guidelines for study conduct and reporting

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Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals

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Transparency of reporting and methodological conduct of prognostic and diagnostic clinical prediction models developed using machine learning in total shoulder arthroplasty: A systematic review and critical appraisal

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When Is a Prognostic Prediction Model Ready for Clinical Use? A Primer for Clinicians

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Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement Using Electronic Health Records

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Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy: Combined machine learning and experimental validation in mice

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Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development

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Invited Commentary: Transparent reporting of artificial intelligence models development and evaluation in surgery: The TRIPOD and DECIDE-AI checklists

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Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data

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Predictive modelling of clinically significant depressive symptoms after coronary artery bypass graft surgery: protocol for a multicentre observational study in two Swiss hospitals (the PsyCor study)

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Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis

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Prediction models for overall survival and all-cause mortality risk in older adults with cancer: a systematic review

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Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study

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A Comprehensive Review of Predictive Precision in Scar Medicine: From Molecular Predictors to Machine Learning Models

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Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnostics

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The application of time-to-event analysis in machine learning prognostic models

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Risk scores in anaesthesia: the future is hard to predict

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Using AI in the Economic Evaluation of AI-Based Health Technologies

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The reporting quality and methodological quality of dynamic prediction models for cancer prognosis

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Clinical prediction models for medication adverse events in patients with rheumatic and musculoskeletal conditions: A systematic literature review

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Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models.

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Using machine learning to define mepolizumab treatment response at 2 years in patients with chronic rhinosinusitis with nasal polyps

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Artificial intelligence–based prognostic models in acute myeloid leukemia: systematic review and meta-analysis

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Development and temporal validation of an interpretable point score for in-hospital mortality in CLL/SLL using the U.S. National Inpatient Sample, 2016–2022

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Sample size calculation for training ensemble machine learning models on health data

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AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics

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Beyond AUC: a clinician’s guide to building and trusting prediction models in oncology—a narrative review

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A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models — part 2: time-to-event outcomes

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Predicting the Objective and Subjective Clinical Outcomes of Anterior Cruciate Ligament Reconstruction: A Machine Learning Analysis of 432 Patients: Response

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Clinical prognostic models for sarcomas: a systematic review and critical appraisal of development and validation studies

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Postoperative Lymph Is a Proximal Source of ctDNA for Detection of Recurrence in HPV-Independent Head and Neck Cancer

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Application of Machine learning in predicting cancer complications using longitudinal Data: A systematic review and Meta-Analysis

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Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database

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Why we need to maintain a critical view on big data and artificial intelligence predictions

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Multimodal PET/CT–MRI Radiomics and Deep Learning Fusion for Individualized Prognostic Prediction in Nasopharyngeal Carcinoma

Cai, Y., Pan, D., Zhang, J., Chen, Z., Chen, K., Sun, C., Su, Y., Li, J., & Yi, H. (2026) · International Journal of Clinical Practice · doi:10.1155/ijcp/1364442 · Cited by 1 · see citing works

Early Detection of Lung Cancer Using Clinical Data and Machine Learning Methods

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A Neutrosophic Topological Approach to Scientific Decision Architectures: Structural Stability, Convergence, and Information Dynamics

Hernández, J. R. H. (2026) · Mathematics · doi:10.3390/math14112002 · Cited by 1 · see citing works

Performance of Machine Learning Models for Predicting Occult Nodal Metastasis in Oral Cavity Squamous Cell Carcinoma: A Systematic Review and Diagnostic Accuracy Meta-Analysis

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Machine Learning - Prediction Models - Methodological conduct - OncologyPreprint

Dhiman, P., & Collins, G. (2021) · doi:10.17605/osf.io/3aezj

Additional file 1 of Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

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Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

Dhiman, P., Ma, J., Navarro, C. L. A., Speich, B., Bullock, G. S., Damen, J. A., Kirtley, S., Hooft, L., Riley, R. D., CALSTER, B. V., et al. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.06.024 · Cited by 109 · see citing works

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

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Additional file 1 of Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

Dhiman, P., Ma, J., Navarro, C. L. A., Speich, B., Bullock, G., Damen, J. A., Hooft, L., Kirtley, S., Riley, R. D., CALSTER, B. V., et al. (2022) · Figshare · doi:10.6084/m9.figshare.20252710