Tufts University

Proceedings of the twenty-first international conference on Machine learning

Carla E. Brodley

2004 · 1.001 Zit.

The role of machine learning in clinical research: transforming the future of evidence generation

E. Hope Weissler, Tristan Naumann, Tomas Andersson et al.

2021 · 276 Zit.

Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities

Jessica K. Paulus, David M. Kent

2020 · 264 Zit.

Digital Medicine: A Primer on Measurement

Andrea Coravos, Jennifer C. Goldsack, Daniel R. Karlin et al.

2019 · 162 Zit.

A Study on the Application and Use of Artificial Intelligence to Support Drug Development

Mary Jo Lamberti, Michael Wilkinson, Bruce A. Donzanti et al.

2019 · 157 Zit.

Mapping clinical reasoning literature across the health professions: a scoping review

Meredith Young, Aliki Thomas, Stuart Lubarsky et al.

2020 · 125 Zit.

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

Shadi Ebrahimian, Mannudeep K. Kalra, Sheela Agarwal et al.

2021 · 116 Zit.

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA

Adrian P. Brady, Bibb Allen, Jaron Chong et al.

2024 · 75 Zit.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Xiang Bai, Hanchen Wang, Liya Ma et al.

2021 · 74 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.

Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup

Marla B. K. Sammer, Yasmin S. Akbari, Richard A. Barth et al.

2023 · 64 Zit.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

Esmée Venema, Benjamin S. Wessler, Jessica K. Paulus et al.

2021 · 60 Zit.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA

Adrian P. Brady, Bibb Allen, Jaron Chong et al.

2024 · 53 Zit.

A collaborative online AI engine for CT-based COVID-19 diagnosis

Yongchao Xu, Liya Ma, Fan Yang et al.

2020 · 47 Zit.

Effect of team training on improving MRI study completion rates and no‐show rates

Alexander Norbash, Kent Yucel, William T. C. Yuh et al.

2016 · 46 Zit.