Sahil Sethi

MD Student and Medical AI Researcher — University of Chicago

Hello! I'm an MD student at the University of Chicago, where I’m part of the BBJ Lab advised by Dr. Brett Beaulieu-Jones. My research focuses on developing deep learning methods for medical time-series data (such as ECGs) with an emphasis on model interpretability and clinical decision support.

I also collaborate with Dr. Lewis Shi and Dr. Nicholas Maassen in the Department of Orthopedic Surgery, where I work on applying machine learning to diagnostic tasks in orthopedics. Before medical school, I earned my B.S. in Biomedical & Health Sciences Engineering from the joint program at UNC-Chapel Hill and NC State, with a concentration in image and signal processing.

Get in touch: sethis[at]uchicago.edu

Selected Publications

*See the full list on Google Scholar

Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs.
S. Sethi*, D. Chen*, M. Burkhart, N. Bhandari, B. Ramadan, & B. Beaulieu-Jones. 2025. arXiv preprint (under review). *Co-first authors

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
S. Sethi, D. Chen, T. Statchen, M. Burkhart, N. Bhandari, B. Ramadan, B. Beaulieu-Jones. 2025. 10th Machine Learning for Healthcare Conference (MLHC), Proceedings of Machine Learning Research 298.

SCOPE-MRI: Bankart Lesion Detection as a Case Study in Data Curation and Deep Learning for Challenging Diagnoses
S. Sethi, S. Reddy, M. Sakarvadia, J. Serotte, D. Nwaudo, N. Maassen, L. Shi. 2025. arXiv preprint (under review).

Toward non-invasive diagnosis of Bankart lesions with deep learning
S. Sethi, S. Reddy, M. Sakarvadia, J. Serotte, D. Nwaudo, N. Maassen, L. Shi. 2025. SPIE Medical Imaging: Computer-Aided Diagnosis.

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