Portrait of Dan Poenaru

Dan Poenaru

Associate Academic Member
dan.poenaru@mcgill.ca
Professor, McGill University, Department of Pediatric Surgery
Research Topics
AI and Healthcare
AI in Health
Medical Machine Learning

Biography

Dan Poenaru is a professor of pediatric surgery at McGill University and a senior scientist at the research institute of the McGill University Health Centre. He has a master’s degrees in health professions education and international development, and a doctorate in health strategy and management. Poenaru is a Fonds de recherche du Québec - Santé (FRQS) and a Canadian Institutes of Health Research (CIHR)-funded investigator in patient-centered surgical care, head of the McGill CommiSur Lab, director of the Jean-Martin Laberge Fellowship in Global Pediatric Surgery, and a founding member of the Global Initiative for Children’s Surgery (GICS).

His current areas of academic interest are technology-assisted surgical communication and medical education, including AI, VR and digital health devices, patient-centred surgical care, and developing global surgical research capacity.

Current Students

Mohsen Amoei
PhD - McGill University
Carmel Daskalo
Master's Research - McGill University
Aylin Erman
PhD - McGill University
Principal supervisor :
Jackie Cheung
Hakima Laribi
PhD - Université de Sherbrooke
Co-supervisor :
Martin Vallières
Website
Github
Olivier Lefebvre
PhD - Université de Sherbrooke
Co-supervisor :
Martin Vallières
Github
Jean Marie Tshimula
Postdoctorate - McGill University
Eve Wang
Master's Research - McGill University
Github

Publications

Family risk communication preferences in pediatric surgery: A scoping review.
Arthega Selvarajan
Brandon Arulanandam
Elena Guadagno
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
Invited commentary on Stoehr J et al: The personal impact of involvement in international global health outreach: A national survey of former operation smile student volunteers.
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
Patient experience or patient satisfaction? A systematic review of child- and family-reported experience measures in pediatric surgery.
Julia Ferreira
Prachikumari Patel
Elena Guadagno
Nikki Ow
Jo Wray
Sherif Emil
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
A rapid review for developing a co-design framework for a pediatric surgical communication application
Michelle Cwintal
Hamed Ranjbar
Parsa Bandamiri
Elena Guadagno
Esli Osmanlliu
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
Screening methods for congenital anomalies in low and lower-middle income countries: A systematic review.
Justina O. Seyi-Olajide
Xiya Ma
Elena Guadagno
Adesoji Ademuyiwa
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
Technology-enhanced trauma training in low-resource settings: A scoping review and feasibility analysis of educational technologies.
Minahil Khan
Fabio Botelho
Laura Pinkham
Elena Guadagno
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.
Amanda Bianco
Zaid A.M. Al-Azzawi
Elena Guadagno
Esli Osmanlliu
Jocelyn Gravel
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
"Your child needs surgery": A survey-based evaluation of simulated expert consent conversations by key stakeholders.
Zoe Atsaidis
Stephan Robitaille
Elena Guadagno
Jeffrey Wiseman
Sherif Emil
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
A debriefing tool to acquire non-technical skills in trauma courses
Fabio Botelho
Jason M. Harley
Natalie Yanchar
Simone Abib
Ilana Bank
Dan Poenaru
2022-11-07
Surgery Open Science (published)
doi.org
The use of artificial intelligence and virtual reality in doctor-patient risk communication: A scoping review.
Ryan Antel
Samira Abbasgholizadeh-Rahimi
Elena Guadagno
Jason M. Harley
Dan Poenaru
2022-10-01
Patient Education and Counseling (published)
doi.org
Application of Artificial Intelligence in Shared Decision Making: Scoping Review
Samira Abbasgholizadeh-Rahimi
Michelle Cwintal
Yuhui Huang
Pooria Ghadiri
Roland Grad
Dan Poenaru
Genevieve Gore
Hervé Tchala Vignon Zomahoun
France Légaré
Pierre Pluye
Background Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared… (see more) decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. Objective We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. Methods We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
2022-08-01
JMIR Medical Informatics (published)
doi.org
Exploring the roles of artificial intelligence in surgical education: A scoping review
Elif Bilgic
Andrew Gorgy
Alison Yang
Michelle Cwintal
Hamed Ranjbar
Kalin Kahla
Dheeksha Reddy
Kexin Li
Helin Ozturk
Eric Zimmermann
Andrea Quaiattini
Samira Abbasgholizadeh-Rahimi
Dan Poenaru
Jason M. Harley
2022-07-01
The American Journal of Surgery (published)
doi.org