Ojus Khanolkar

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About

Ojus Khanolkar is a researcher at the forefront of applying machine learning to surgical decision-making, with a primary focus on kidney transplantation. His work bridges artificial intelligence and clinical practice, most notably through the development of a predictive decision tree model that analyzes the feasibility of open versus robotic kidney transplant. This model, built on data from nearly 1,000 patients, offers a data-driven tool to optimize surgical approach selection, potentially improving patient outcomes and operational efficiency. Though early in its citation impact, this 2024 study has already garnered 3 citations, signaling growing interest in his innovative approach. Khanolkar’s contributions lie in demonstrating how machine learning can enhance precision in complex surgical planning, a field where such tools remain nascent. His research is particularly valuable for students and clinicians exploring the integration of AI into transplant surgery, as it provides a clear framework for evaluating surgical feasibility. With a focus on practical, outcome-oriented applications, Khanolkar is establishing himself as a key voice in the evolving dialogue between computational methods and surgical innovation.