ArticleDiagnostic and prognostic research2022
Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.
Article in Diagnostic and prognostic research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 8 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 8 syntheses or guidelines pooled it.
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- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis.Scientific reports · 2025Pooled it
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.Journal of patient-reported outcomes · 2024Pooled it
- Clinical predictive models for recurrence and survival in treated laryngeal and hypopharyngeal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.European radiology · 2026Article
- Reproducibility in biomedical research.Nature medicine · 2026Review
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Machine Learning-Based Survival Prediction in Early-Stage Non-Small Cell Lung Cancer: Development and Cross-National External Validation.Journal of clinical medicine · 2026Article
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
- Immunosuppressive regimens and long-term kidney transplant outcomes: A dual survival modeling framework.PloS one · 2026Article
- Prospective multi-centre evaluation of guideline-based artificial intelligence to streamline multidisciplinary tumour board.Frontiers in oncology · 2026Article
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- Risk of bias in machine learning and statistical models to predict height or weight: a systematic review in fetal and paediatric medicine.Diagnostic and prognostic research · 2025Review
- Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023).Journal of medical Internet research · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundPrognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain.
methodsWe conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately.
resultsWe included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation.
conclusionsThe quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.
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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.