Portrait de Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

Membre académique associé
Professeure adjointe, McGill University, Département de médecine de famille
Sujets de recherche
Apprentissage automatique médical
Exploration des données
IA en santé
IA et santé
Optimisation
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Biographie

Dre Samira A. Rahimi, B. Ing., Ph. D., est titulaire d’une Chaire de recherche du Canada (niveau II) en intelligence artificielle et en soins de santé primaires numériques avancés. Elle est professeure adjointe à la Faculté de médecine dentaire et des sciences de la santé bucco-dentaire de l’Université McGill, codirectrice du McGill Collaborative for AI and Society (McCAIS) et membre académique associée de Mila – Institut québécois d’intelligence artificielle. Elle est également codirectrice de la recherche du programme de résidence en pratique générale (RPG) en dentisterie à l’Hôpital général juif.

Grâce à sa formation interdisciplinaire, ses recherches, soutenues par des financements nationaux et internationaux, portent sur le développement et la mise en œuvre de technologies de santé numériques avancées, notamment d’outils basés sur l’intelligence artificielle, afin d’améliorer les soins de santé primaires. Elle s’engage particulièrement à améliorer les soins destinés aux populations vulnérables et mal desservies, veillant à ce que l’innovation bénéficie à toutes et à tous.

Ses travaux 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 du Canada (CRSNG), Roche Canada, la Fondation Brocher (Suisse) et les Instituts de recherche en santé du Canada (IRSC).

Elle est lauréate de nombreux prix, dont le prix Marjorie Bowman et Robert Choplin du nouvel investigateur 2022, qui récompense les contributions exceptionnelles de chercheurs émergents dans le domaine de la recherche en soins de santé primaires.

Étudiants actuels

Sydney Keyamo
Maîtrise recherche - McGill
Chetan Shirvankar
Doctorat - McGill
Ting Wang
Doctorat - McGill

Publications

Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review
Ting Wang
Elham Emami
Dana Jafarpour
Raymond Tolentino
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
The lack of Equity, Diversity, and Inclusion (EDI) principles in the lifecycle of Artificial Intelligence (AI) technologies in healthcare is… (voir plus) a growing concern. Despite its importance, there is still a gap in understanding the initiatives undertaken to address this issue. This review aims to explore what and how EDI principles have been integrated into the design, development, and implementation of AI studies in healthcare. We followed the scoping review framework by Levac et al. and the Joanna Briggs Institute. A comprehensive search was conducted until April 29, 2022, across MEDLINE, Embase, PsycInfo, Scopus, and SCI-EXPANDED. Only research studies in which the integration of EDI in AI was the primary focus were included. Non-research articles were excluded. Two independent reviewers screened the abstracts and full texts, resolving disagreements by consensus or by consulting a third reviewer. To synthesize the findings, we conducted a thematic analysis and used a narrative description. We adhered to the PRISMA-ScR checklist for reporting scoping reviews. The search yielded 10,664 records, with 42 studies included. Most studies were conducted on the American population. Previous research has shown that AI models improve when socio-demographic factors such as gender and race are considered. Despite frameworks for EDI integration, no comprehensive approach systematically applies EDI principles in AI model development. Additionally, the integration of EDI into the AI implementation phase remains under-explored, and the representation of EDI within AI teams has been overlooked. This review reports on what and how EDI principles have been integrated into the design, development, and implementation of AI technologies in healthcare. We used a thorough search strategy and rigorous methodology, though we acknowledge limitations such as language and publication bias. A comprehensive framework is needed to ensure that EDI principles are considered throughout the AI lifecycle. Future research could focus on strategies to reduce algorithmic bias, assess the long-term impact of EDI integration, and explore policy implications to ensure that AI technologies are ethical, responsible, and beneficial for all.
2025-07-01
PLOS Digital Health (publié)
doi.org
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.
Shayan nejadshamsi
Vania Karami
Negar Ghourchian
Narges Armanfard
Howard Bergman
Roland Grad
Machelle Wilchesky
Vladimir Khanassov
Isabelle Vedel
Samira Abbasgholizadeh-Rahimi
BACKGROUND Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early … (voir plus)detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. OBJECTIVE This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi-based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model's predictions and identify key features associated with depression classification. METHODS In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi-based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults' Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model's predictions. RESULTS A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were "average sleep duration," "total number of sleep interruptions," "percentage of nights with sleep interruptions," "average duration of sleep interruptions," and "Edmonton Frailty Scale." CONCLUSIONS The findings from this preliminary study demonstrate the feasibility of using Wi-Fi-based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study.
2025-03-03
JMIR Aging (publié)
doi.org
EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study
Samira Abbasgholizadeh-Rahimi
Richa Shrivastava
Anita Brown-Johnson
Pascale Caidor
Claire Davies
Amal Idrissi Janati
Pascaline Kengne Talla
Sreenath Madathil
Bettina M Willie
Elham Emami
2024-11-15
Journal of Medical Internet Research (publié)
doi.org
Outcomes of guidelines from health technology assessment organizations in community-based primary care: a systematic mixed studies review
Ashkan Baradaran
Raymond Tolentino
Roland Grad
Isabelle Ganache
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Pierre Pluye
2024-11-14
International Journal of Technology Assessment in Health Care (publié)
doi.org
Perspectives on virtual interviews and emerging technologies integration in family medicine residency programs: a cross-sectional survey study
Raymond Tolentino
Charo Rodriguez
Fanny Hersson-Edery
Julie Lane
Samira Abbasgholizadeh-Rahimi
2024-09-09
BMC Medical Education (publié)
doi.org
Existing Digital Health Technology Index Summary Report for Older Adults Living with Neurocognitive Disorders (Mild and Major) and Their Informal Caregivers: An Environmental Scan
Ambily Jose
Maxime Sasseville
Ellen Gorus
Anik Giguère
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Clémence Balley
Ronald Buyl
Marie-Pierre Gagnon
Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and … (voir plus)their informal caregivers. (1) Background: It is important to obtain a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will aid the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences based on trustable information. (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in the grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized the findings. (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed for identifying current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.
2024-06-22
Geriatrics (publié)
doi.org
Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada
Mehdi Mousavi
Shabnam Shafiee
Jason M Harley
Jackie Cheung
Samira Abbasgholizadeh-Rahimi
Introduction The application of large language models such as generative pre-trained transformers (GPTs) has been promising in medical educa… (voir plus)tion, and its performance has been tested for different medical exams. This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems (SAMPs) from the certification exam of the College of Family Physicians of Canada (CFPC). Method Between August 8th and 25th, 2023, we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website. Two independent certified family physician reviewers scored AI-generated responses twice: first, according to the CFPC answer key (ie, CFPC score), and second, based on their knowledge and other references (ie, Reviews’ score). An ordinal logistic generalised estimating equations (GEE) model was applied to analyse repeated measures across the five rounds. Result According to the CFPC answer key, 607 (73.6%) lines of answers by GPT-3.5 and 691 (81%) by GPT-4 were deemed accurate. Reviewer’s scoring suggested that about 84% of the lines of answers provided by GPT-3.5 and 93% of GPT-4 were correct. The GEE analysis confirmed that over five rounds, the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5 (OR: 2.31; 95% CI: 1.53 to 3.47; p0.001). Similarly, the Reviewers’ Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed th
2024-05-01
Family Medicine and Community Health (publié)
doi.org
Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada
Mehdi Mousavi
Shabnam Shafiee
Jason M. Harley
Jackie Cheung
Samira Abbasgholizadeh-Rahimi
Introduction The application of large language models such as generative pre-trained transformers (GPTs) has been promising in medical educa… (voir plus)tion, and its performance has been tested for different medical exams. This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems (SAMPs) from the certification exam of the College of Family Physicians of Canada (CFPC). Method Between August 8th and 25th, 2023, we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website. Two independent certified family physician reviewers scored AI-generated responses twice: first, according to the CFPC answer key (ie, CFPC score), and second, based on their knowledge and other references (ie, Reviews’ score). An ordinal logistic generalised estimating equations (GEE) model was applied to analyse repeated measures across the five rounds. Result According to the CFPC answer key, 607 (73.6%) lines of answers by GPT-3.5 and 691 (81%) by GPT-4 were deemed accurate. Reviewer’s scoring suggested that about 84% of the lines of answers provided by GPT-3.5 and 93% of GPT-4 were correct. The GEE analysis confirmed that over five rounds, the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5 (OR: 2.31; 95% CI: 1.53 to 3.47; p0.001). Similarly, the Reviewers’ Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed th
2024-05-01
Family Medicine and Community Health (publié)
doi.org
Implications of conscious AI in primary healthcare
Dorsai Ranjbari
Samira Abbasgholizadeh-Rahimi
The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of A… (voir plus)I identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients’, healthcare workers’ and policy-makers’ attitudes towards consciousness of AI systems in primary healthcare settings.
2024-03-01
Family Medicine and Community Health (publié)
doi.org
Implications of conscious AI in primary healthcare
Dorsai Ranjbari
Samira Abbasgholizadeh-Rahimi
2024-03-01
Family Medicine and Community Health (publié)
doi.org
Implications of conscious AI in primary healthcare
Dorsai Ranjbari
Samira Abbasgholizadeh-Rahimi
The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of A… (voir plus)I identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients’, healthcare workers’ and policy-makers’ attitudes towards consciousness of AI systems in primary healthcare settings.
2024-03-01
Family Medicine and Community Health (publié)
doi.org
Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.
Vania Karami
Mark J. Yaffe
Genevieve Gore
AJung Moon
Samira Abbasgholizadeh-Rahimi
2024-03-01
Archives of gerontology and geriatrics (Print) (publié)
doi.org