Broad Institute

Synthetic data in machine learning for medicine and healthcare

Richard J. Chen, Ming Y. Lu, Tiffany Chen et al.

2021 · 683 Zit.

Artificial intelligence for multimodal data integration in oncology

Jana Lipková, Richard J. Chen, Bowen Chen et al.

2022 · 655 Zit.

Algorithmic fairness in artificial intelligence for medicine and healthcare

Richard J. Chen, Judy J. Wang, Drew F. K. Williamson et al.

2023 · 554 Zit.

Metrics reloaded: recommendations for image analysis validation

Lena Maier‐Hein, Annika Reinke, Patrick Godau et al.

2024 · 380 Zit.

A guide to artificial intelligence for cancer researchers

Raquel Pérez-López, Narmin Ghaffari Laleh, Faisal Mahmood et al.

2024 · 175 Zit.

Understanding metric-related pitfalls in image analysis validation

Annika Reinke, Minu D. Tizabi, Michael Baumgartner et al.

2024 · 166 Zit.

A manifesto on explainability for artificial intelligence in medicine

Carlo Combi, Beatrice Amico, Riccardo Bellazzi et al.

2022 · 148 Zit.

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

William Lotter, Michael J. Hassett, Nikolaus Schultz et al.

2024 · 145 Zit.

Federated benchmarking of medical artificial intelligence with MedPerf

Alexandros Karargyris, Renato Umeton, Micah Sheller et al.

2023 · 141 Zit.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Robin Borchert, Tiago Azevedo, AmanPreet Badhwar et al.

2023 · 100 Zit.

Demographic bias in misdiagnosis by computational pathology models

Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson et al.

2024 · 98 Zit.

Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records

Zongqi Xia, E.A. Secor, Lori B. Chibnik et al.

2013 · 81 Zit.

To do no harm — and the most good — with AI in health care

Carey Beth Goldberg, Laura Adams, David Blumenthal et al.

2024 · 69 Zit.

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Cheuk To Chung, Sharen Lee, Emma King et al.

2022 · 69 Zit.

Machine-learning Prognostic Models from the 2014–16 Ebola Outbreak: Data-harmonization Challenges, Validation Strategies, and mHealth Applications

Andrés Colubri, Mary‐Anne Hartley, Matthew Siakor et al.

2019 · 58 Zit.