University of Virginia
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen et al.
2022 · 460 Zit.
To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis
Sarah Lebovitz, Hila Lifshitz‐Assaf, Natalia Levina
2022 · 439 Zit.
Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment
Narendra N. Khanna, Mahesh Maindarkar, Vijay Viswanathan et al.
2022 · 320 Zit.
ChatGPT: Applications, Opportunities, and Threats
Aram Bahrini, Mohammadsadra Khamoshifar, Hossein Abbasimehr et al.
2023 · 273 Zit.
The Janus Effect of Generative AI: Charting the Path for Responsible Conduct of Scholarly Activities in Information Systems
Anjana Susarla, Ram D. Gopal, Jason Bennett Thatcher et al.
2023 · 233 Zit.
Artificial Intelligence and the Future of Surgical Robotics
Sandip S. Panesar, Yvonne Cagle, Divya Chander et al.
2019 · 198 Zit.
The reproducibility crisis in the age of digital medicine
Aaron Stupple, David Singerman, Leo Anthony Celi
2019 · 143 Zit.
Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine
Matthew G. Hanna, Liron Pantanowitz, Rajesh Dash et al.
2025 · 127 Zit.
Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making
Kori Inkpen, Shreya Chappidi, Keri Mallari et al.
2023 · 102 Zit.
Electronic health records and clinician burnout: A story of three eras
Kevin B. Johnson, Michael J. Neuss, Don Eugene Detmer
2020 · 93 Zit.
Demographic bias in misdiagnosis by computational pathology models
Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson et al.
2024 · 92 Zit.
Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging
Riccardo Fogliato, Shreya Chappidi, Matthew P. Lungren et al.
2022 · 82 Zit.
Introduction to Artificial Intelligence and Machine Learning for Pathology
James H. Harrison, John R. Gilbertson, Matthew G. Hanna et al.
2021 · 82 Zit.
Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review
Jasjit S. Suri, Mrinalini Bhagawati, Sudip Paul et al.
2022 · 81 Zit.
A “datathon” model to support cross-disciplinary collaboration
Jérôme Aboab, Leo Anthony Celi, Peter Charlton et al.
2016 · 80 Zit.