A5069286448- Pediatric Urology and Nephrology Studies
- Bladder and Urothelial Cancer Treatments
- Artificial Intelligence in Healthcare and Education
- Urological Disorders and Treatments
- Prostate Cancer Diagnosis and Treatment
- Cardiac, Anesthesia and Surgical Outcomes
- Urinary Tract Infections Management
- Prostate Cancer Treatment and Research
- Renal cell carcinoma treatment
- Colorectal Cancer Screening and Detection
- Surgical Simulation and Training
- Hormonal and reproductive studies
- Urinary and Genital Oncology Studies
- Autopsy Techniques and Outcomes
- Radiomics and Machine Learning in Medical Imaging
- Machine Learning in Healthcare
- Urinary Bladder and Prostate Research
- Mobile Health and mHealth Applications
- Diversity and Career in Medicine
- Sexual Differentiation and Disorders
- Meta-analysis and systematic reviews
- Multiple and Secondary Primary Cancers
- Renal and Vascular Pathologies
- Influenza Virus Research Studies
- Health Systems, Economic Evaluations, Quality of Life
University of Toronto
2018-2025
University Health Network
2022-2025
Public Health Ontario
2019-2025
Hospital for Sick Children
2022-2024
Artificial Intelligence in Medicine (Canada)
2024
SickKids Foundation
2022-2023
Princess Margaret Cancer Centre
2023
Fudan University Shanghai Cancer Center
2023
Oxfam
2023
Liechtenstein Institute
2023
As more artificial intelligence (AI) applications are integrated into healthcare, there is an urgent need for standardization and quality-control measures to ensure a safe successful transition of these novel tools clinical practice. We describe the role silent trial, which evaluates AI model on prospective patients in real-time, while end-users (i.e., clinicians) blinded predictions such that they do not influence decision-making. present our experience evaluating previously developed...
Exposure to research data and artificial intelligence (AI) model predictions may lead many sources of bias in clinical decision-making evaluation. These include anchoring bias, automation leakage. In this case study, we introduce a new source termed "induced belief revision," which have discovered through our experience developing testing an AI predict obstructive hydronephrosis children based on their renal ultrasounds. After silent trial model, observed unintentional but clinically...
No AccessJournal of UrologyPediatric Urology1 Dec 2022Multi-institutional Validation Improved Vesicoureteral Reflux Assessment With Simple and Machine Learning ApproachesThis article is commented on by the following:Editorial Comment Adree Khondker, Jethro C. Kwong, Priyank Yadav, Justin Y. H. Chan, Anuradha Singh, Marta Skreta, Lauren Erdman, Daniel T. Keefe, Katherine Fischer, Gregory Tasian, Jessica Hannick, Frank Papanikolaou, Benjamin J. Cooper, Christopher S. Mandy Rickard, Armando...
We aimed to develop an explainable machine learning (ML) model predict side-specific extraprostatic extension (ssEPE) identify patients who can safely undergo nerve-sparing radical prostatectomy using preoperative clinicopathological variables.A retrospective sample of data from 900 prostatic lobes at our institution was used as the training cohort. Primary outcome presence ssEPE. The baseline for comparison had highest performance out current biopsy-derived predictive models A separate...