Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review. - Jasjit S. Suri; Mrinalini Bhagawati; Sudip Paul; Athanasios Protogeron; Petros P. Sfikakis; George D. Kitas; Narendra N. Khanna; Zoltan Ruzsa; Aditya M. Sharma 001; Sanjay Saxena; Gavino Faa; Kosmas I. Paraskevas; et al. (Comput. Biol. Medicine, 2022)

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias. - Jasjit S. Suri; Mrinalini Bhagawati; Sushant Agarwal; Sudip Paul; Amit Pandey; Suneet K. Gupta 001; Luca Saba; Kosmas I. Paraskevas; Narendra N. Khanna; John R. Laird; Amer M. Johri; Manudeep K. Kalra; et al. (IEEE Access, 2023)

Artificial intelligence bias in medical system designs: a systematic review. - Ashish Kumar; Vivekanand Aelgani; Rubeena Vohra; Suneet K. Gupta 001; Mrinalini Bhagawati; Sudip Paul; Luca Saba; Neha Suri; Narendra N. Khanna; John R. Laird; Amer M. Johri; Manudeep Kalra; et al. (Multim. Tools Appl., 2024)

Attention-based hybrid deep learning models and its scientific validation for cardiovascular disease risk stratification. - Mrinalini Bhagawati; Siddharth Gupta; Sudip Paul; Laura E. Mantella; Amer M. Johri; John R. Laird; Ekta Tiwari; Narendra N. Khanna; Andrew Nicolaides; Rajesh Singh; Mustafa Al-Maini; Luca Saba; et al. (Biomed. Signal Process. Control., 2025)