MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

no code implementations • 27 Aug 2023 • Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo P. Reis, Rahul Thapa, Louis Blankemeier, Julian Z. Genkins, Ethan Steinberg, Ashwin Nayak, Birju S. Patel, Chia-Chun Chiang, Alison Callahan, Zepeng Huo, Sergios Gatidis, Scott J. Adams, Oluseyi Fayanju, Shreya J. Shah, Thomas Savage, Ethan Goh, Akshay S. Chaudhari, Nima Aghaeepour, Christopher Sharp, Michael A. Pfeffer, Percy Liang, Jonathan H. Chen, Keith E. Morse, Emma P. Brunskill, Jason A. Fries, Nigam H. Shah

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.

2k Instruction Following +2

Automatic classification of multiple catheters in neonatal radiographs with deep learning

no code implementations • 14 Nov 2020 • Robert D. E. Henderson, Xin Yi, Scott J. Adams, Paul Babyn

Performance was similar for the set of 58 test images consisting of 2 or more catheters, with an AP of 0. 975 (0. 255-1. 000) for NGTs, 0. 997 (0. 009-1. 000) for ETTs, 0. 981 (0. 797-0. 998) for UACs, and 0. 937 (0. 689-0. 990) for UVCs.

General Classification

Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?

1 code implementation • 9 Feb 2020 • Xin Yi, Scott J. Adams, Robert D. E. Henderson, Paul Babyn

Catheters are the second most common abnormal finding on radiographs.