Portrait de Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

samira.rahimi@mila.quebec
Professeure adjointe, McGill University, Département de génie électrique et informatique
Sujets de recherche
Apprentissage automatique médical
Graphes de connaissances
Traitement du langage naturel
Site web
LinkedIn
X

Biographie

Samira Abbasgholizadeh-Rahimi (B. Ing., Ph. D.) est titulaire de la Chaire de recherche du Canada sur les soins de santé primaires numériques avancés et professeure adjointe au Département de médecine familiale de l'Université McGill et à Mila – Institut québécois d'intelligence artificielle.

Elle est également scientifique affiliée à l'Institut Lady Davis de recherches médicales de l'Hôpital général juif, présidente élue de la Société canadienne de recherche opérationnelle et directrice d'Intelligence artificielle en médecine familiale (AIFM).

Bénéficiant de sa formation interdisciplinaire, ses travaux portent sur le développement et la mise en œuvre de technologies de santé numérique avancées, telles que les outils d'aide à la décision basés sur l'IA, dans les soins de santé primaires. Ses recherches sont consacrées à l'amélioration de la prévention et de la gestion des maladies chroniques, dont les maladies cardiovasculaires, avec un accent particulier sur les populations vulnérables.

Les travaux qu’elle a menés en tant que chercheuse principale ont été financés par le Fonds de recherche du Québec - Santé (FRQS), le Conseil de recherches en sciences naturelles et en génie (CRSNG), Roche Canada, la Fondation Brocher (Suisse) et la Stratégie de recherche axée sur le patient (SRAP) - Instituts de recherche en santé du Canada (IRSC).

Elle a reçu de nombreux prix, notamment le prix New Investigator Primary Care Research 2022 du North American Primary Care Research Group (NAPCRG), qui récompense les contributions exceptionnelles de nouveaux chercheurs dans le domaine de la recherche sur les soins primaires.

Étudiants actuels

Sydney Keyamo
Maîtrise recherche - McGill
Shayan nejadshamsi
Stagiaire de recherche - McGill
Haowei Qiu
Maîtrise professionnelle - McGill
Gauri Sharma
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
AJung Moon
Github
Google Scholar
Chetan Shirvankar
Doctorat - McGill
chetan.shirvankar@mila.quebec

Publications

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 (publié)
doi.org
Integrating Equity, Diversity, and Inclusion throughout the lifecycle of Artificial Intelligence in health
Milka Nyariro
Elham Emami
Samira Abbasgholizadeh-Rahimi
Health care systems are the infrastructures that are put together to deliver health and social services to the population at large. These or… (voir plus)ganizations are increasingly applying Artificial Intelligence (AI) to improve the efficiency and effectiveness of health and social care. Unfortunately, both health care systems and AI are confronted with a lack of Equity, Diversity, and Inclusion (EDI). This short paper focuses on the importance of integrating EDI concepts throughout the life cycle of AI in health. We discuss the risks that the lack of EDI in the design, development and implementation of AI-based tools might have on the already marginalized communities and populations in the healthcare setting. Moreover, we argue that integrating EDI principles and practice throughout the lifecycle of AI in health has an important role in achieving health equity for all populations. Further research needs to be conducted to explore how studies in AI-health have integrated.
2022-06-29
13th Augmented Human International Conference (publié)
doi.org
Ageism and Artificial Intelligence: Protocol for a Scoping Review
Charlene H Chu
Kathleen Leslie
Jiamin Shi
Rune Nyrup
Andria Bianchi
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
Alexandra Lyn
Amanda Grenier
Background Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention… (voir plus) has been paid to algorithmic biases toward older adults. Objective This paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications. Methods The scoping review follows a 6-stage methodology framework developed by Arksey and O’Malley. The search strategy has been established in 6 databases. We will investigate the legal implications of ageism in AI by searching grey literature databases, targeted websites, and popular search engines and using an iterative search strategy. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full text, and meet one of the following two additional criteria: (1) include “bias” related to AI in any application (eg, facial recognition) and (2) discuss bias related to the concept of old age or ageism. At least two reviewers will independently conduct the title, abstract, and full-text screening. Search results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guideline. We will chart data on a structured form and conduct a thematic analysis to highlight the societal, legal, and ethical implications reported in the literature. Results The database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022. Conclusions The findings will provide interdisciplinary insights into the extent of age-related bias in AI systems. The results will contribute foundational knowledge that can encourage multisectoral cooperation to ensure that AI is developed and deployed in a manner consistent with ethical values and human rights legislation as it relates to an older and aging population. We will publish the review findings in peer-reviewed journals and disseminate the key results with stakeholders via workshops and webinars. Trial Registration OSF Registries AMG5P; https://osf.io/amg5p International Registered Report Identifier (IRRID) DERR1-10.2196/33211
2022-06-01
JMIR Research Protocols (publié)
doi.org
Moving shared decision making forward in Iran.
Samira Abbasgholizadeh-Rahimi
Nam Nguyen
Mahasti Alizadeh
Dan Poenaru
2022-05-01
Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen (publié)
doi.org
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
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 (publié)
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 (publié)
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… (voir plus)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
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 (publié)
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 (publié)
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 (publié)
doi.org