Papers
8
Total Citations
47
H-Index
4
About
Jessica Dai is a clinician-researcher at the forefront of integrating artificial intelligence with robotic urologic surgery. Her work spans three interconnected domains: surgical video analysis using deep learning, clinical outcomes of robotic-assisted procedures, and advanced imaging for renal and prostate pathology. Dai pioneered the use of multi-task convolutional neural networks to objectively evaluate surgical skill from robotic-assisted surgery videos, developing cascaded architectures that assess tumor resection and renography steps during partial nephrectomy—work that has garnered over 20 citations. Her clinical research includes landmark studies on irreversible electroporation for small renal masses, reporting 5-year outcomes that demonstrate this nonthermal ablation technology's potential to overcome thermal limitations. She has also developed predictive MRI models for benign prostatic hyperplasia histology and investigated the Clear Cell Likelihood Score to reduce benign pathology rates in nephron-sparing surgery for chronic kidney disease patients. With publications in high-impact urology journals and presentations at major conferences like the American Urological Association, Dai's research is shaping how machine learning can enhance surgical training and patient selection for minimally invasive urologic oncology procedures.
Research Focus
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