Portrait de Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

Membre académique associé
Professeure adjointe, McGill University, Département de médecine dentaire
Sujets de recherche
Apprentissage automatique médical
Exploration des données
IA en santé
IA et santé
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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
Shayan Nejadshamsi
Postdoctorat - McGill
Google Scholar
Chetan Shirvankar
Doctorat - McGill
Ting Wang
Doctorat - McGill

Publications

Family caregivers' acceptance of Artificial Intelligence-enabled technologies for providing care to older adults
Amanda Yee
Mark J. Yaffe
Tibor Schuster
Sylvie Lambert
Samira Abbasgholizadeh-Rahimi
Artificial intelligence (AI)-enabled technologies hold promise for assisting in the care of an aging population. Few studies have focused on… (voir plus) exploring family caregivers’ (FCGs) behavioural intention of using such innovation, and even fewer have employed a technology acceptance framework. This study examined FCGs of older adults’ behavioural intention of using AI-enabled technologies for caregiving. We conducted a theory-based cross-sectional quantitative survey. Eligible FCGs for this study were: (1) aged 45–64; (2) residing in Quebec, Canada; (3) providing care for at least one older adult (65+); (4) having access to a computer or smartphone with internet connectivity; and, (5) having proficiency in reading and comprehending English or French. We adapted and expanded the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to measure their behavioural intention of using AI-enabled technologies for caregiving. We used descriptive statistics and a random forest model to assess the most important predictive factors across nine variables and their direction of association with behavioural intention. The Consensus-Based Checklist for Reporting of Survey Studies (CROSS) guidelines was used for reporting the study’s results. Among the polling firm’s 100,000 panelists, 2740 eligible individuals were randomly chosen to receive an email invitation to the study. Of 465 panelists who opened the survey (i.e., unique visitors),199 were eligible and completed the online survey. The random forest model explained between 56% and 86% of the behavioural intention variance of using AI, with social influence demonstrating the highest predictive relevance as indicated by a 35% increase in mean-squared error once removed from the model. Among the nine variables considered, six demonstrated a positive association with behavioural intention. These variables included social influence, effort expectancy, performance expectancy, perceived trust, confidence in healthcare professionals’ advice for the use of AI-enabled technologies, and facilitating connditions. The variables perceived cost and technology anxiety indicated a negative association with behavioural intention. Our extended UTAUT model identified factors associated with FCGs' intention to use AI. While all nine variables contributed, attitudes toward AI within caregivers’ social circles was the strongest predictor. Stakeholders from industry, government, and healthcare can enhance the adoption of AI-enabled technologies in older adult care by leveraging facilitators and addressing barriers experienced by caregivers.
2025-12-29
BMC Geriatrics (publié)
doi.org
Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation
Shayan Nejadshamsi
Charlene H. Chu
Katherine S. McGilton
Xiaoxiao Li
Charlene Ronquillo
Samira Abbasgholizadeh-Rahimi
The COVID-19 pandemic has highlighted the growing reliance on machine learning (ML) models for predicting disease severity, which is importa… (voir plus)nt for clinical decision-making and equitable resource allocation. While achieving high predictive accuracy is important, ensuring fairness in the prediction output of these models is equally important to prevent bias-driven disparities in healthcare. This study evaluates fairness in a machine learning-based COVID-19 severity classification model and proposes an Explainable AI (XAI)-based bias mitigation strategy to address sex-related bias. Using data from the Quebec Biobank, we developed an XGBoost-based multi-class classification model. Fairness was assessed using Subset Accuracy Parity Difference (SAPD) and Label-wise Equal Opportunity Difference (LEOD) metrics. Four bias mitigation strategies were implemented and evaluated: Fair Representation Learning, Fair Classifier Using Constraints, Adversarial Debiasing, and our proposed XAI-based method utilizing SHapley Additive exPlanations (SHAP) method for feature importance analysis. The study cohort included 1642 COVID-19 positive older adults (mean age: 77.5), balance equally between males and females. The baseline (unmitigated) classification model achieved 90.68% accuracy but exhibited a 10.11% Subset Accuracy Parity Difference between sexes, indicating a relatively large bias. The introduced XAI-based method demonstrated a better trade-off between model performance and fairness compared to existing bias mitigation methods by identifying sex-sensitive feature interactions and integrating them into the model re-training. Traditional fairness interventions often compromise accuracy to a greater extent. Our XAI-based method achieves the best balance between classification performance and bias, enhancing its clinical applicability. The XAI-driven bias mitigation intervention effectively reduces sex-based disparities in COVID-19 severity prediction without the significant accuracy loss observed in traditional methods. This approach provides a framework for developing fair and accurate clinical decision support systems for older adults, which ensures equitable care in clinical risk stratification and resource allocation.
2025-12-03
JAMIA Open (publié)
doi.org
Tracking the Evolving Role of Artificial Intelligence in Implementation Science: Protocol for a Living Scoping Review of Applications, Evaluation Approaches and Outcomes
Guillaume Fontaine
Olivia Di Lalla
Susan Michie
Byron J. Powell
Vivian Welch
James Thomas
Jeffery Chan
Samira Abbasgholizadeh-Rahimi
France Légaré
Janna Hastings
Sylvie D. Lambert
Justin Presseau
Sharon E. Straus
Ian D. Graham
Ruopeng An
Daniel N. Elakpa
Meagan Mooney
Alenda Dwiadila Matra Putra
Rachael Laritz
Natalie Taylor
Background Artificial intelligence (AI) offers significant opportunities to improve the field of implementation science by supporting… (voir plus) key activities such as evidence synthesis, contextual analysis, and decision-making to promote the adoption and sustainability of evidence-based practices. This living scoping review aims to: (1) map applications of AI in implementation research and practice; (2) identify evaluation approaches, reported outcomes, and potential risks; and (3) synthesize reported research gaps and opportunities for advancing the use of AI in implementation science. Methods This scoping review will follow the Joanna Briggs Institute (JBI) methodology and the Cochrane guidance for living systematic reviews. A living scoping review is warranted to keep up with the rapid changes in AI and its growing use in implementation science. We will include empirical studies, systematic reviews, grey literature, and policy documents that describe or evaluate applications of AI to support implementation science across the steps of the Knowledge-to-Action (KTA) Model. AI methods and models of interest include machine learning, deep learning, natural language processing, large language models, and related technologies and approaches. A search strategy will be applied to bibliographic databases (MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, Web of Science), relevant journals, conference proceedings, and preprint servers. Two reviewers will independently screen studies and extract data on AI characteristics, specific implementation task according to the KTA Model, evaluation methods, outcome domains, risks, and research gaps. Extracted data will be analyzed descriptively and synthesized narratively using a mapping approach aligned with the KTA Model. Discussion This living review will consolidate the evidence base on how AI is applied across the spectrum of implementation science. It will inform researchers, policymakers, and practitioners seeking to harness AI to improve the adoption, scale-up, and sustainability of evidence-based interventions, while identifying areas for methodological advancement and risk mitigation. Review registration Open Science Framework, May 2025: https://doi.org/10.17605/OSF.IO/2Q5DV
2025-10-16
F1000Research (publié)
doi.org
Use of Artificial Intelligence in Adolescents’ Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions
Gauri Sharma
Mark J Yaffe
Pooria Ghadiri
Rushali Gandhi
Laura Pinkham
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Abstract Background Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate manageme… (voir plus)nt are needed to decrease mortality and morbidity. Artificial intelligence’s (AI) potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents’ mental health. Objective This review aimed to identify AI interventions that have been tested, implemented, or both, for use in adolescents’ mental health care. Methods We used the Arksey and O’Malley framework, further refined by Levac et al, along with the Joanna Briggs Institute methodology, to guide this scoping review. We searched 5 electronic databases from the inception date through July 2024 (inclusive). Four independent reviewers screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a fifth reviewer was sought. We evaluated the risk of bias (ROB) for prognosis and diagnosis-related studies using the Prediction Model Risk of Bias Assessment Tool. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting. Results Of the papers screened, 88 papers relevant to our eligibility criteria were identified. Among the included papers, AI was most commonly used for diagnosis (n=78), followed by monitoring and evaluation (n=19), treatment (n=10), and prognosis (n=6). As some studies addressed multiple applications, categories are not mutually exclusive. For diagnosis, studies primarily addressed suicidal behaviors (n=11) and autism spectrum disorder (n=7). Machine learning was the most frequently reported AI method across all application areas. The overall ROB for diagnostic and prognostic models was predominantly unclear (58%), while 20% of studies had a high ROB and 22% were assessed as low risk. Conclusions In our review, we found that AI is being applied across various areas of adolescent mental health care, spanning diagnosis, treatment planning, symptom monitoring, and prognosis. Interestingly, most studies to date have concentrated heavily on diagnostic tools, leaving other important aspects of care relatively underexplored. This presents a key opportunity for future research to broaden the scope of AI applications beyond diagnosis. Moreover, future studies should emphasize the meaningful and active involvement of end users in the design, development, and validation of AI interventions, alongside improved transparency in reporting AI models, data handling, and analytical processes to build trust and support safe clinical implementation.
2025-06-05
JMIR Mental Health (publié)
doi.org
Use of Artificial Intelligence in Adolescents' Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions
Gauri Sharma
Mark J Yaffe
Pooria Ghadiri
Rushali Gandhi
Laura Pinkham
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Given the increasing prevalence of mental health problems among adolescents, early intervention and appropriate management are needed to dec… (voir plus)rease mortality and morbidity. Artificial intelligence’s (AI) potential contributions, although significant in the field of medicine, have not been adequately studied in the context of adolescents’ mental health. This review aimed to identify AI interventions that have been tested, implemented, or both, for use in adolescents’ mental health care. We used the Arksey and O’Malley framework, further refined by Levac et al, along with the Joanna Briggs Institute methodology, to guide this scoping review. We searched 5 electronic databases from the inception date through July 2024 (inclusive). Four independent reviewers screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a fifth reviewer was sought. We evaluated the risk of bias (ROB) for prognosis and diagnosis-related studies using the Prediction Model Risk of Bias Assessment Tool. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting. Of the papers screened, 88 papers relevant to our eligibility criteria were identified. Among the included papers, AI was most commonly used for diagnosis (n=78), followed by monitoring and evaluation (n=19), treatment (n=10), and prognosis (n=6). As some studies addressed multiple applications, categories are not mutually exclusive. For diagnosis, studies primarily addressed suicidal behaviors (n=11) and autism spectrum disorder (n=7). Machine learning was the most frequently reported AI method across all application areas. The overall ROB for diagnostic and prognostic models was predominantly unclear (58%), while 20% of studies had a high ROB and 22% were assessed as low risk. In our review, we found that AI is being applied across various areas of adolescent mental health care, spanning diagnosis, treatment planning, symptom monitoring, and prognosis. Interestingly, most studies to date have concentrated heavily on diagnostic tools, leaving other important aspects of care relatively underexplored. This presents a key opportunity for future research to broaden the scope of AI applications beyond diagnosis. Moreover, future studies should emphasize the meaningful and active involvement of end users in the design, development, and validation of AI interventions, alongside improved transparency in reporting AI models, data handling, and analytical processes to build trust and support safe clinical implementation.
2025-06-05
JMIR Mental Health (publié)
doi.org
AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study
Raymond Tolentino
Fanny Hersson-Edery
Mark Yaffe
Samira Abbasgholizadeh-Rahimi
As health care moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of artificia… (voir plus)l intelligence (AI). However, family medicine training in AI has often been inconsistent or lacking. The aim of the study is to develop a curriculum framework for family medicine postgraduate education on AI called “Artificial Intelligence Training in Postgraduate Family Medicine Education” (AIFM-ed). First, we conducted a comprehensive scoping review on existing AI education frameworks guided by the methodological framework developed by Arksey and O’Malley and Joanna Briggs Institute methodological framework for scoping reviews. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Next, 2 national expert panels were conducted. Panelists included family medicine educators and residents knowledgeable in AI from family medicine residency programs across Canada. Participants were purposively sampled, and panels were held via Zoom, recorded, and transcribed. Data were analyzed using content analysis. We followed the Standards for Reporting Qualitative Research for panels. An integration of the scoping review results and 2 panel discussions of 14 participants led to the development of the AIFM-ed curriculum framework for AI training in postgraduate family medicine education with five key elements: (1) need and purpose of the curriculum, (2) learning objectives, (3) curriculum content, (4) organization of curriculum content, and (5) implementation aspects of the curriculum. Using the results of this study, we developed the AIFM-ed curriculum framework for AI training in postgraduate family medicine education. This framework serves as a structured guide for integrating AI competencies into medical education, ensuring that future family physicians are equipped with the necessary skills to use AI effectively in their clinical practice. Future research should focus on the validation and implementation of the AIFM-ed framework within family medicine education. Institutions also are encouraged to consider adapting the AIFM-ed framework within their own programs, tailoring it to meet the specific needs of their trainees and health care environments.
2025-04-24
JMIR Medical Education (publié)
doi.org
Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review (Preprint)
Raymond Tolentino
Ashkan Baradaran
Genevieve Gore
Pierre Pluye
Samira Abbasgholizadeh-Rahimi
BACKGROUND

The successful integration of artificial intelligence (AI) in… (voir plus)to clinical practice is contingent upon physicians’ comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.

 OBJECTIVE

The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.

 METHODS

We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.

 RESULTS

Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.

 CONCLUSIONS

This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs.

 INTERNATIONAL REGISTERED REPORT
2024-07-17
JMIR Medical Education (publié)
doi.org
089 Levers and limitations of artificial intelligence (AI) to support the assessment and implementation of shared decision making (SDM): perspectives of key stakeholders
Anik Giguère
Adrian Edwards
Denitza Williams
France Légaré
Natalie Joseph-Williams
Samira Abbasgholizadeh-Rahimi
Karina Prévost
Marie-Clare Hunter
Anna Torrens-Burton
Justine Laloux
2024-06-30
International Symposium on Data Mining (publié)
doi.org
Primary care physicians' perceptions of artificial intelligence systems in the care of adolescents' mental health
Pooria Ghadiri
Mark J. Yaffe
Alayne Mary Adams
Samira Abbasgholizadeh-Rahimi
Given that mental health problems in adolescence may have lifelong impacts, the role of primary care physicians (PCPs) in identifying and ma… (voir plus)naging these issues is important. Artificial Intelligence (AI) may offer solutions to the current challenges involved in mental health care. We therefore explored PCPs’ challenges in addressing adolescents’ mental health, along with their attitudes towards using AI to assist them in their tasks. We used purposeful sampling to recruit PCPs for a virtual Focus Group (FG). The virtual FG lasted 75 minutes and was moderated by two facilitators. A life transcription was produced by an online meeting software. Transcribed data was cleaned, followed by a priori and inductive coding and thematic analysis. We reached out to 35 potential participants via email. Seven agreed to participate, and ultimately four took part in the FG. PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients’ data, and relatively credible. They envisioned AI assisting with tasks such as diagnoses and establishing treatment plans. However, they feared that reliance on AI might result in a loss of clinical competency. PCPs wanted AI systems to be user-friendly, and they were willing to assist in achieving this goal if it was within their scope of practice and they were compensated for their contribution. They stressed a need for regulatory bodies to deal with medicolegal and ethical aspects of AI and clear guidelines to reduce or eliminate the potential of patient harm. This study provides the groundwork for assessing PCPs’ perceptions of AI systems’ features and characteristics, potential applications, possible negative aspects, and requirements for using them. A future study of adolescents’ perspectives on integrating AI into mental healthcare might contribute a fuller understanding of the potential of AI for this population. The online version contains supplementary material available at 10.1186/s12875-024-02417-1.
2024-06-12
BMC Primary Care (publié)
doi.org
Moving shared decision-making forward in Iran
Samira Abbasgholizadeh-Rahimi
Nam Nguyen
Mahasti Alizadeh
Dan Poenaru
2022-04-30
Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen (publié)
doi.org