Portrait of Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

Associate Academic Member
Assistant Professor, McGill University, Department of Electrical and Computer Engineering
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Biography

Samira Abbasgholizadeh-Rahimi (BEng, PhD) is the Canada Research Chair in Advanced Digital Primary Health Care, an assistant professor in the Department of Family Medicine at McGill University and an associate academic member at Mila – Quebec Artificial Intelligence Institute.

Rahimi is an affiliated scientist at Lady Davis Institute for Medical Research at the Jewish General Hospital, the elected president of the Canadian Operational Research Society, and director of Artificial Intelligence in Family Medicine (AIFM).

Drawing on her interdisciplinary background, her research focuses on the development and implementation of advanced digital health technologies, such as AI-enabled decision support tools, in primary health care. Her research is dedicated to enhancing the prevention and management of chronic diseases, such as cardiovascular disease, with a particular emphasis on vulnerable populations.

Rahimi‘s work as a principal investigator has been funded by the Fonds de recherche du Québec – Santé (FRQS), the Natural Sciences and Engineering Research Council (NSERC), Roche Canada, the Brocher Foundation (Switzerland), and the Strategy for Patient-Oriented Research (SPOR) of the Canadian Institutes of Health Research (CIHR).

She is the recipient of numerous awards, including the 2022 New Investigator Primary Care Research Award of North American Primary Care Research Group (NAPCRG), which recognizes exceptional contributions by emerging investigators in the field of primary care research.

Current Students

Chetan Shirvankar
PhD - McGill University
chetan.shirvankar@mila.quebec
Gauri Sharma
Master's Research - McGill University
Principal supervisor :
AJung Moon
gauri.sharma@mila.quebec
Github
Google Scholar
Vania Karami
Postdoctorate - McGill University
vania.karami@mila.quebec

Publications

Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Samantha Dequanter
Maaike Fobelets
Iris Steenhout
Marie-Pierre Gagnon
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Ronald Buyl
Ellen Gorus
2022-04-28
BMC Geriatrics (published)
doi.org
User Experience of a Computer-Based Decision Aid for Prenatal Trisomy Screening: Mixed Methods Explanatory Study
Titilayo Tatiana Agbadje
Chantale Pilon
Pierre Bérubé
Jean‐claude Forest
François Rousseau
Samira Abbasgholizadeh-Rahimi
Yves Giguère
France Légaré
Background Mobile health tools can support shared decision-making. We developed a computer-based decision aid (DA) to help pregnant women an… (see more)d their partners make informed, value-congruent decisions regarding prenatal screening for trisomy. Objective This study aims to assess the usability and usefulness of computer-based DA among pregnant women, clinicians, and policy makers. Methods For this mixed methods sequential explanatory study, we planned to recruit a convenience sample of 45 pregnant women, 45 clinicians from 3 clinical sites, and 15 policy makers. Eligible women were aged >18 years and >16 weeks pregnant or had recently given birth. Eligible clinicians and policy makers were involved in prenatal care. We asked the participants to navigate a computer-based DA. We asked the women about the usefulness of the DA and their self-confidence in decision-making. We asked all participants about usability, quality, acceptability, satisfaction with the content of the DA, and collected sociodemographic data. We explored participants’ reactions to the computer-based DA and solicited suggestions. Our interview guide was based on the Mobile App Rating Scale. We performed descriptive analyses of the quantitative data and thematic deductive and inductive analyses of the qualitative data for each participant category. Results A total of 45 pregnant women, 14 clinicians, and 8 policy makers participated. Most pregnant women were aged between 25 and 34 years (34/45, 75%) and White (42/45, 94%). Most clinicians were aged between 35 and 44 years (5/14, 36%) and women (11/14, 79%), and all were White (14/14, 100%); the largest proportion of policy makers was aged between 45 and 54 years (4/8, 50%), women (5/8, 62%), and White (8/8, 100%). The mean usefulness score for preparing for decision-making for women was 80/100 (SD 13), and the mean self-efficacy score was 88/100 (SD 11). The mean usability score was 84/100 (SD 14) for pregnant women, 77/100 (SD 14) for clinicians, and 79/100 (SD 23) for policy makers. The mean global score for quality was 80/100 (SD 9) for pregnant women, 72/100 (SD 12) for clinicians, and 80/100 (SD 9) for policy makers. Regarding acceptability, participants found the amount of information just right (52/66, 79%), balanced (58/66, 88%), useful (38/66, 58%), and sufficient (50/66, 76%). The mean satisfaction score with the content was 84/100 (SD 13) for pregnant women, 73/100 (SD 16) for clinicians, and 73/100 (SD 20) for policy makers. Participants thought the DA could be more engaging (eg, more customizable) and suggested strategies for implementation, such as incorporating it into clinical guidelines. Conclusions Pregnant women, clinicians, and policy makers found the DA usable and useful. The next steps are to incorporate user suggestions for improving engagement and implementing the computer-based DA in clinical practice.
2022-04-14
JMIR Pediatrics and Parenting (published)
doi.org
User Experience of a Computer-Based Decision Aid for Prenatal Trisomy Screening: Mixed Methods Explanatory Study
Titilayo Tatiana Agbadje
Chantale Pilon
Pierre Bérubé
Jean-Claude Forest
François Rousseau
Samira Abbasgholizadeh-Rahimi
Yves Giguère
France Légaré
2022-04-14
JMIR Pediatrics and Parenting (published)
doi.org
GCNFusion: An efficient graph convolutional network based model for information diffusion
Bahare Fatemi
Soheila Mehr Molaei
Shirui Pan
Samira Abbasgholizadeh-Rahimi
2022-04-01
Expert systems with applications (published)
doi.org
Application of AI in community based primary health care: Systematic review and critical appraisal
Samira Abbasgholizadeh-Rahimi
Patrick Archambault
Hervé Tchala Vignon Zomahoun
Sam Chandavong
Marie-Pierre Gagnon
Sabrina M. Wong
Gauri Sharma
Lyse Langlois
Nathalie Rheault
Yves Couturier
Jean Légaré
2022-03-01
Systematic review, meta-analysis, or scoping review (published)
doi.org
Quantum-Inspired Interpertable AI-Empowered Decision Support System for Detection of Early-Stage Rheumatoid Arthritis in Primary Care Using Scarce Dataset
Samira Abbasgholizadeh-Rahimi
Mojtaba Kolahdoozi
Arka Mitra
Jose L Salmeron
Amir-Mohammad Navali
Alireza Sadeghpour
Amir Mir Mir Mohammadi
2022-02-03
Mathematics (published)
doi.org
Evaluation of a prenatal screening decision aid: A mixed methods pilot study.
Titilayo Tatiana Agbadje
Samira Abbasgholizadeh-Rahimi
Mélissa Côté
Andrée-Anne Tremblay
Mariama Penda Diallo
Hélène Elidor
Alex Poulin Herron
Codjo Djignefa Djade
France Légaré
2022-02-01
Patient Education and Counseling (published)
doi.org
Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
A. Grenier
Abstract Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, edu… (see more)cation, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2022-01-20
The gerontologist (published)
doi.org
Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
Amanda Grenier
Abstract Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, edu… (see more)cation, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2022-01-20
The Gerontologist (published)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
Andrew Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
2021-12-10
Surgical Innovation (published)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
A. Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
Arti fi cial intelligence (AI) based devices are currently being used in the delivery of surgical care in a variety of settings. 1,2 Howeve… (see more)r, AI-enabled systems can trigger a variety of opinions and emotions, which reveals the different lenses that shape views on AI. Nonethless, work within surgical education may necessitate a more balanced view; with an acknowledgment of the participation of AI-enhanced devices in the delivery of surgical care and education
2021-12-10
Surgical Innovation (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
2021-11-30
American Journal of Surgery (published)
doi.org