a2z-1 for Multi-Disease Detection in Abdomen-Pelvis CT: External Validation and Performance Analysis Across 21 Conditions

no code implementations • 17 Dec 2024 • Pranav Rajpurkar, Julian N. Acosta, Siddhant Dogra, Jaehwan Jeong, Deepanshu Jindal, Michael Moritz, Samir Rajpurkar

We present a comprehensive evaluation of a2z-1, an artificial intelligence (AI) model designed to analyze abdomen-pelvis CT scans for 21 time-sensitive and actionable findings.

The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports

no code implementations • 16 Dec 2024 • Julián N. Acosta, Siddhant Dogra, Subathra Adithan, Kay Wu, Michael Moritz, Stephen Kwak, Pranav Rajpurkar

Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times.

Diagnostic

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

1 code implementation • 4 Aug 2020 • Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.

COVID-19 Diagnosis Decision Making +2