Dr. Kevin Dick (he/they) is an Investigator (level II) at the Children’s Hospital of Eastern Ontario Research Institute (CHEO-RI) and Artificial Intelligence Data Scientist at BORN Ontario. He is the Principal Investigator of an interdisciplinary research lab advancing AI for maternal-fetal and population health.
They also lead AI modernization efforts at BORN Ontario through the development of explainable AI (XAI) and privacy-preserving machine learning frameworks towards population-wide screening for early prediction of adverse pregnancy and neonatal outcomes.
Dr. Dick’s research bridges biomedical informatics, machine learning, and high-performance computing to design scalable, interpretable, and clinically integrated AI systems. Their work spans multimodal model development, health data interoperability, and the creation of next-generation AI infrastructures capable of linking provincial and national health data securely. He has pioneered frameworks that advance explainability, reproducibility, and transparency in population screening and medical AI.
As a mentor and collaborator, they supervise interdisciplinary teams of trainees, researchers, and engineers to advance theoretical AI methodology and develop real-world AI applications that enhance early screening, diagnosis, and patient outcomes. He is deeply committed to open science, equity in AI applications, and the responsible evolution of medical AI systems.
Dr. Dick earned his PhD in Biomedical Engineering and is the recipient of the Governor General’s Gold Medal. Their long-term vision is to build federated, ethically grounded AI ecosystems that transform how population health data is analyzed, shared, and acted upon to improve care across Canada and beyond.
Research Projects
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Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data
04/07/2025
This study aims to determine whether machine learning models applied to health administrative and birth registry data can identify young children (aged 18 months to 5 years) who are at increased likelihood of developing ASD.
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Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis
19/04/2024
This study emphasizes the potential of deep learning models in predicting kidney anomalies from limited prenatal ultrasound imagery. The proposed adaptations in model representation and interpretation represent a novel solution to multi-class prediction problems.
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The transformative potential of AI in obstetrics and gynaecology
27/03/2024
This study explores the use and potential of AI in three focus areas: predictive modelling for pregnancy complications, Deep learning-based image interpretation for precise diagnoses, and large language models enabling intelligent health care assistants.