ArticleNPJ digital medicine2025
A scoping review of robustness concepts for machine learning in healthcare.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review.JMIR aging · 2025Pooled it
- From optical control to translational readiness: an evidence-tiered framework for optogenetics in neuromuscular and neurological disorders.Materials today. Bio · 2026Review
- Development and internal validation of an interpretable machine learning prognostic prediction model for postoperative delirium after esophagectomy.Journal of thoracic disease · 2026Article
- Toward trustworthy healthcare AI: designing academic research for translation readiness.Molecular systems biology · 2026Review
- HEART-Watch: A multimodal physiological dataset from a Google Pixel Watch across different physical states.PLOS digital health · 2026Article
- Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance-A Scoping Review of Reviews.Healthcare (Basel, Switzerland) · 2026Review
- Designing machine learning-based models to predict residual pain after functional treatment of acute lateral ankle ligamentous sprains.Journal of orthopaedic surgery and research · 2026Article
- Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response.Cancer immunology, immunotherapy : CII · 2026Article
- Interpretable LightGBM model with SHAP analysis predicts non-excellent response to initial radioiodine therapy in differentiated thyroid carcinoma.Journal of applied clinical medical physics · 2026Article
- TRIAGE: Trustworthy Reporting and Assessment for Clinical Gain and Effectiveness of AI Models.Diagnostics (Basel, Switzerland) · 2026Review
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective.Frontiers in medicine · 2026Article
- AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review.JMIR mHealth and uHealth · 2025Article
- Robustness of steroidomics-based machine learning for diagnosis of primary aldosteronism: a laboratory medicine perspective.Clinical chemistry and laboratory medicine · 2025Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- Fatty acid-binding protein 4 as a biomarker for colon adenocarcinoma risk and prognosis: Challenges and future directions.World journal of gastrointestinal oncology · 2025Article
- A scoping review of robustness concepts for machine learning in healthcare.NPJ digital medicine · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While machine learning (ML)-based solutions-often referred to as artificial intelligence (AI) solutions-have demonstrated comparable or superior performance to human experts across various healthcare applications, their vulnerability to perturbations and stability to variations due to new environments-essentially, their robustness-remains ambiguous and often overlooked. In this review, we aimed to identify the types of robustness addressed in the literature for ML models in healthcare. A total of 274 eligible records were retrieved from PubMed, Web of Science, IEEE Xplore, and additional sources. Eight general concepts of robustness emerged. Furthermore, an analysis of those concepts across types of data and types of predictive models revealed that the concepts were differently addressed. Our findings offer valuable insights for stakeholders seeking to understand and navigate the robustness of machine learning models during their development, validation, and deployment in healthcare settings, where interpretation of robustness may vary.
Identifiers
What OpenQuestion 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 OpenQuestion graph.