Matthew Engelhard

Duke University, Charlottesville Medical Research

Papers

Total Citations

H-Index

About

Matthew Engelhard is a pioneering researcher at the intersection of robotics, deep learning, and clinical diagnostics. His primary contributions lie in developing autonomous systems for medical imaging and surgical skill assessment. His most notable work, "RobOCTNet," introduces a robotically aligned optical coherence tomography (RAOCT) system coupled with a deep learning model to detect referable posterior segment pathology in emergency department patients. This innovation, already garnering 6 citations since its 2024 publication, promises to streamline ophthalmic triage by enabling rapid, automated screening without specialized personnel. Earlier, Engelhard contributed to surgical education through "MP14-07," a study analyzing attention and movement patterns in novice versus expert robotic surgeons, earning 3 citations. This work highlights his broader interest in quantifying surgical expertise to improve training. Engelhard’s research is characterized by its translational impact—bridging cutting-edge AI and robotics with real-world clinical needs. By automating complex diagnostic tasks and refining surgical skill assessment, he is shaping a future where technology enhances both accuracy and accessibility in healthcare. His interdisciplinary approach makes him a key figure for students and researchers exploring AI-driven medical innovation.