University of Toronto

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 · 1.734 Zit.

Foundation models for generalist medical artificial intelligence

Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad et al.

2023 · 1.458 Zit.

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Xiaoxuan Liu, Samantha Cruz Rivera, David Moher et al.

2020 · 951 Zit.

Do no harm: a roadmap for responsible machine learning for health care

Jenna Wiens, Suchi Saria, Mark Sendak et al.

2019 · 938 Zit.

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Laleh Seyyed-Kalantari, Haoran Zhang, Matthew B. A. McDermott et al.

2021 · 720 Zit.

Federated learning for predicting clinical outcomes in patients with COVID-19

Ittai Dayan, Holger R. Roth, Aoxiao Zhong et al.

2021 · 687 Zit.

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions

Alaa Abd‐Alrazaq, Rawan AlSaad, Dari Alhuwail et al.

2023 · 626 Zit.

Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology

Jeremy Petch, Shuang Di, Walter Nelson

2021 · 526 Zit.

Applications of artificial neural networks in health care organizational decision-making: A scoping review

Nida Shahid, Tim Rappon, Whitney Berta

2019 · 523 Zit.

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Lu Xu, Leslie Sanders, Kay Li et al.

2021 · 507 Zit.

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

An Tang, Roger Tam, Alexandre Cadrin-Chênevert et al.

2018 · 503 Zit.

AI recognition of patient race in medical imaging: a modelling study

Judy Wawira Gichoya, Imon Banerjee, Ananth Reddy Bhimireddy et al.

2022 · 488 Zit.

Transparency and reproducibility in artificial intelligence

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

2020 · 484 Zit.

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.

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Baptiste Vasey, Myura Nagendran, Bruce Campbell et al.

2022 · 456 Zit.