Gaël Varoquaux

Machine learning for medical imaging: methodological failures and recommendations for the future

2022 · 544 Zit. · npj Digital Medicine

Metrics reloaded: recommendations for image analysis validation

2024 · 344 Zit. · Nature Methods

Understanding metric-related pitfalls in image analysis validation

2024 · 154 Zit. · Nature Methods

Preventing dataset shift from breaking machine-learning biomarkers

2021 · 87 Zit. · GigaScience

Metrics reloaded: Recommendations for image analysis validation

2022 · 62 Zit. · arXiv (Cornell University)

Understanding metric-related pitfalls in image analysis validation

2023 · 20 Zit. · arXiv (Cornell University)

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

2024 · 12 Zit. · arXiv (Cornell University)

Confidence Intervals Uncovered: Are We Ready for Real-World Medical Imaging AI?

2024 · 9 Zit. · Lecture notes in computer science

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

2025 · 9 Zit. · The Lancet Digital Health

How I failed machine learning in medical imaging -- shortcomings and recommendations

2021 · 4 Zit. · arXiv (Cornell University)

Advancing science- and evidence-based AI policy

2025 · 4 Zit. · Science

Lessons from shortcomings in machine learning for medical imaging

2023 · 2 Zit. · OECD eBooks

Beyond Quantification: Navigating Uncertainty in Professional AI Systems

2025 · 1 Zit. · RSS Data Science and Artificial Intelligence

False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims

2025 · 1 Zit. · ArXiv.org

Narrative review on the clinical evaluation of AI-based digital medical devices from a methodological perspective

2025 · 0 Zit. · SPIRE - Sciences Po Institutional REpository