Maarten van Smeden

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

2024 · 1.636 Zit. · BMJ

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

2021 · 758 Zit. · BMJ Open

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

2022 · 458 Zit. · npj Digital Medicine

Time to reality check the promises of machine learning-powered precision medicine

2020 · 236 Zit. · The Lancet Digital Health

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

2025 · 230 Zit. · BMJ

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

2022 · 130 Zit. · European Heart Journal

Methodology over metrics: current scientific standards are a disservice to patients and society

2021 · 102 Zit. · Journal of Clinical Epidemiology

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

2022 · 94 Zit. · Journal of Clinical Epidemiology

Five critical quality criteria for artificial intelligence-based prediction models

2023 · 64 Zit. · European Heart Journal

The year in cardiovascular medicine 2021: digital health and innovation

2021 · 51 Zit. · European Heart Journal

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

2025 · 27 Zit. · The Lancet Digital Health

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care

2024 · 23 Zit. · JAMA Network Open

Machine Learning and Statistics in Clinical Research Articles—Moving Past the False Dichotomy

2023 · 22 Zit. · JAMA Pediatrics

Artificial Intelligence and Statistics: Just the Old Wine in New Wineskins?

2022 · 20 Zit. · Frontiers in Digital Health

Performance evaluation of predictive AI models to support medical\n decisions: Overview and guidance

2024 · 13 Zit. · arXiv (Cornell University)