Brigham and Women's Hospital

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

Gary S. Collins, Karel G.M. Moons, Paula Dhiman et al.

2024 · 1.839 Zit.

Artificial intelligence in cancer imaging: Clinical challenges and applications

Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath et al.

2019 · 1.805 Zit.

Machine Learning methods for Quantitative Radiomic Biomarkers

Chintan Parmar, Patrick Großmann, Johan Bussink et al.

2015 · 1.008 Zit.

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Jonathan Waring, Charlotta Lindvall, Renato Umeton

2020 · 828 Zit.

Synthetic data in machine learning for medicine and healthcare

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

2021 · 680 Zit.

The Potential of Radiomic-Based Phenotyping in Precision Medicine

Hugo J.W.L. Aerts

2016 · 657 Zit.

Artificial intelligence for multimodal data integration in oncology

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

2022 · 653 Zit.

Medical 3D Printing for the Radiologist

Dimitris Mitsouras, Peter Liacouras, Amir Imanzadeh et al.

2015 · 614 Zit.

Algorithmic fairness in artificial intelligence for medicine and healthcare

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

2023 · 549 Zit.

Surgical data science for next-generation interventions

Lena Maier‐Hein, S. Swaroop Vedula, Stefanie Speidel et al.

2017 · 516 Zit.

Evaluation of symptom checkers for self diagnosis and triage: audit study

Hannah L. Semigran, Jeffrey A. Linder, Courtney A. Gidengil et al.

2015 · 506 Zit.

Transparency and reproducibility in artificial intelligence

Benjamin Haibe‐Kains, George Alexandru Adam, Ahmed Hosny et al.

2020 · 488 Zit.

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

Joeky T. Senders, Patrick Staples, Aditya V. Karhade et al.

2017 · 460 Zit.

The RSNA Pediatric Bone Age Machine Learning Challenge

Safwan S. Halabi, Luciano M. Prevedello, Jayashree Kalpathy–Cramer et al.

2018 · 453 Zit.

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study

Travis Zack, Eric Lehman, Mirac Süzgün et al.

2023 · 427 Zit.