Carnegie Mellon University Ai Major Triumphs In Automated Medical Diagnosis Challenge

Dongkyun Kim, a junior artificial intelligence major from Carnegie Mellon University’s School of Computer Science, designed the winning deep-learning model in a recent competition to accurately classify diseases based on chest X-rays. Kim used a transformer model to fuse information from multiple images of one patient and his model, CheXFusion, could reliably detect diseases with a high level of accuracy while minimizing the number of false alarms. Kim, an undergraduate, rented the use of a single GPU to compete on his own against teams of graduate students from other top universities and his model drastically outperformed other solutions, placing first in all three metrics used in the competition.