Valentina Ngai, Ph.D., P.Eng.
About
Valentina Ngai, Ph.D., P.Eng., is a researcher whose work spans health technology, artificial intelligence in medicine, and pediatric health services. Her published work addresses how AI and machine learning tools are designed, evaluated, and reported in clinical contexts, with particular attention to issues of diversity and inclusivity in medical imaging datasets. A systematic review she co-authored examined the representativeness of mammography datasets used in AI development, and other work has assessed how well randomized controlled trials of AI interventions conform to CONSORT-AI reporting standards.
Alongside her AI-focused research, Dr. Ngai has contributed to studies on care for children with medical complexity, including work on parental self-efficacy, symptom burden, and a nurse-led mobile application designed to support symptom management in this population. Her publications have appeared in journals including Nature Communications, Infection Control and Hospital Epidemiology, the Journal of Advanced Nursing, and Clinical Imaging. She has also contributed to educational initiatives, including a pilot course aimed at training early-career neurosurgeons in the critical appraisal of AI and machine learning methods.
Her dual credentials in research and engineering, reflected in her doctoral and professional engineering designations, position her at the intersection of clinical evidence and technical methodology.
Publications
- Associations among parental self-efficacy, symptom burden in children with medical complexity, and their use of health services.
- Corrigendum to "Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review" [Clin Imaging 118 (2025) 110369].
- COVID-19 prevention training with video-based feedback in nursing homes: impact on staff safety behaviors.
- Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review.
- Nurse-led proactive mobile application in symptom management for children with medical complexity: Study protocol for a randomized controlled trial.
- Author Correction: Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.
- Empowering Early Career Neurosurgeons in the Critical Appraisal of Artificial Intelligence and Machine Learning: The Design and Evaluation of a Pilot Course.
- Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.
- Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis.
- High inter-follicular spatial co-localization of CD8+FOXP3+ with CD4+CD8+ cells predicts favorable outcome in follicular lymphoma.
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