San Francisco VA Medical Center

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

Milena Gianfrancesco, Suzanne Tamang, Jinoos Yazdany et al.

2018 · 1.265 Zit.

Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review

Albert T. Young, Dominic Amara, A. Bhattacharya et al.

2021 · 317 Zit.

Developing a data sharing community for spinal cord injury research

Alison Callahan, Kim D. Anderson, Michael S. Beattie et al.

2017 · 55 Zit.

Next-Generation Artificial Intelligence for Diagnosis

Julia Adler‐Milstein, Jonathan H. Chen, Gurpreet Dhaliwal

2021 · 50 Zit.

Confounders mediate AI prediction of demographics in medical imaging

Grant Duffy, Shoa L. Clarke, Matthew Christensen et al.

2022 · 43 Zit.

Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research

Austin Chou, Abel Torres‐Espín, J. Russell Huie et al.

2022 · 29 Zit.

Seeing the Unseen: Advancing Generative AI Research in Radiology

Woojin Kim

2024 · 19 Zit.

Modernizing Medical Device Regulation: Challenges and Opportunities for the 510(k) Clearance Process

Kushal T. Kadakia, Vinay K. Rathi, Sanket S. Dhruva et al.

2024 · 15 Zit.

Impact of hybrid supervision approaches on the performance of artificial intelligence for the classification of chest radiographs

Ryan J. Ellis, Erik Ellestad, Brett M. Elicker et al.

2020 · 9 Zit.

Around the <scp>EQUATOR</scp> with <scp>Clin‐STAR</scp> : Prediction modeling opportunities and challenges in aging research

W. James Deardorff, L. Grisell Diaz‐Ramirez, W. John Boscardin et al.

2023 · 5 Zit.

Digitally decentralised mock oral board examination for neurological surgery trainees

Christopher S. Graffeo, Benjamin D. Elder, Jamie J. Van Gompel et al.

2020 · 4 Zit.

A use case of ChatGPT: summary of an expert panel discussion on electronic health records and implementation science

Seppo T. Rinne, Julian Brunner, Timothy P. Hogan et al.

2024 · 1 Zit.

Artificial Intelligence to Propel Evidence Generation

Sanket S. Dhruva, Lesley Curtis, Robert M. Califf

2026 · 0 Zit.

All That Shines Is Not Gold: Maintaining Scientific Rigor When Evaluating, Interpreting, and Reviewing Studies Using Large Language Models

Nicole E. Dundas, Tyler J. Law, Teva D. Brender et al.

2025 · 0 Zit.

Large language models accurately extract aortic information from abdominal imaging reports in a large, real-world database

Colleen P. Flanagan, Lawrence D. Gerstley, Steven Okuhn et al.

2025 · 0 Zit.