Just in Time Radiology Decision Support Using Real-time Data Feeds. - John L. Burns; Dan Hasting; Judy W. Gichoya; Ben McKibben; Lindsey Shea; Mark Frank (J. Digit. Imaging, 2020)

Using Machine Learning Approaches to Identify Exercise Activities from a Triple-Synchronous Biomedical Sensor. - Yohan Mahajan; Jahnavi Pinnamraju; John L. Burns; Judy W. Gichoya; Saptarshi Purkayastha (ISDA, 2021)

A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals. - Ju Sun; Le Peng; Taihui Li; Dyah Adila; Zach Zaiman; Genevieve B. Melton; Nicholas Ingraham; Eric Murray; Daniel Boley; Sean Switzer; John L. Burns; Kun Huang; et al. (CoRR, 2021)

Reading Race: AI Recognises Patient's Racial Identity In Medical Images. - Imon Banerjee; Ananth Reddy Bhimireddy; John L. Burns; Leo Anthony Celi; Li-Ching Chen; Ramon Correa; Natalie Dullerud; Marzyeh Ghassemi; Shih-Cheng Huang; Po-Chih Kuo; Matthew P. Lungren; Lyle J. Palmer; et al. (CoRR, 2021)

Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets. - Ananth Reddy Bhimireddy; John Lee Burns; Saptarshi Purkayastha; Judy Wawira Gichoya (CoRR, 2022)

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals. - Le Peng; Gaoxiang Luo; Andrew Walker; Zachary Zaiman; Emma K. Jones; Hemant Gupta; Kristopher Kersten; John L. Burns; Christopher A. Harle; Tanja Magoc; Benjamin Shickel; Scott D. Steenburg; et al. (J. Am. Medical Informatics Assoc., 2022)