Portrait de Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

Membre académique associé
Professeure adjointe, McGill University, Département de génie électrique et informatique

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

Doctorat - McGill University
Maîtrise recherche - McGill University
Superviseur⋅e principal⋅e :
Postdoctorat - McGill University
Stagiaire de recherche - McGill University
Co-superviseur⋅e :

Publications

Implications of conscious AI in primary healthcare
Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.
Vania Karami
Mark J. Yaffe
Genevieve Gore
Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol
Sathya Karunananthan
Arya Rahgozar
Ramtin Hakimjavadi
Hui Yan
Kunal A Dalsania
Howard Bergman
Bishwajit Ghose
Jim LaPlante
Tess McCutcheon
Daniel I McIsaac
Nadia Sourial
Manpreet Thandi
Sabrina T Wong
Clare Liddy
Qualitative Code Suggestion: A Human-Centric Approach to Qualitative Coding
Qualitative coding is a content analysis method in which researchers read through a text corpus and assign descriptive labels or qualitative… (voir plus) codes to passages. It is an arduous and manual process which human-computer interaction (HCI) studies have shown could greatly benefit from NLP techniques to assist qualitative coders. Yet, previous attempts at leveraging language technologies have set up qualitative coding as a fully automatable classification problem. In this work, we take a more assistive approach by defining the task of qualitative code suggestion (QCS) in which a ranked list of previously assigned qualitative codes is suggested from an identified passage. In addition to being user-motivated, QCS integrates previously ignored properties of qualitative coding such as the sequence in which passages are annotated, the importance of rare codes and the differences in annotation styles between coders. We investigate the QCS task by releasing the first publicly available qualitative coding dataset, CVDQuoding, consisting of interviews conducted with women at risk of cardiovascular disease. In addition, we conduct a human evaluation which shows that our systems consistently make relevant code suggestions.
Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.
Elham Emami
Milka Nyariro
Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (voir plus) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.
Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.
Elham Emami
Milka Nyariro
Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (voir plus) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.
Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review
Lily Puterman-Salzman
Jory Katz
Howard Bergman
Roland Grad
Vladimir Khanassov
Genevieve Gore
Isabelle Vedel
Machelle Wilchesky
Negar Ghourchian
Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence … (voir plus)(AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O’Malley’s and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson’s or Huntington’s disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.
Integrating equity, diversity and inclusion throughout the lifecycle of AI within healthcare: a scoping review protocol
Milka Nyariro
Elham Emami
Pascale Caidor
Age-related bias and artificial intelligence: a scoping review
Charlene H Chu
Simon Donato-Woodger
Shehroz S Khan
Rune Nyrup
Kathleen Leslie
Alexandra Lyn
Tianyu Shi
Andria Bianchi
Amanda Grenier
Artificial Intelligence in COVID-19-Related Geriatric Care: A Scoping Review
Emina Burnazovic
Amanda Yee
Joshua Howard Levy
Genevieve Gore
Curriculum frameworks and educational programs in artificial intelligence for medical students, residents, and practicing physicians: a scoping review protocol.
Raymond Tolentino
Ashkan Baradaran
Genevieve Gore
Pierre Pluye
OBJECTIVE The aim of this scoping review is to synthesize knowledge from the literature on curriculum frameworks and current educational pro… (voir plus)grams that focus on the teaching and learning of artificial intelligence (AI) for medical students, residents, and practicing physicians. INTRODUCTION To advance the implementation of AI in clinical practice, physicians need to have a better understanding of AI and how to use it within clinical practice. Consequently, medical education must introduce AI topics and concepts into the curriculum. Curriculum frameworks are educational road maps to teaching and learning. Therefore, any existing AI curriculum frameworks must be reviewed and, if none exist, such a framework must be developed. INCLUSION CRITERIA This review will include articles that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of articles and study designs will be included, except conference abstracts and protocols. METHODS This review will follow the JBI methodology for scoping reviews. Keywords will first be identified from relevant articles. Another search will then be conducted using the identified keywords and index terms. The following databases will be searched: MEDLINE (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (EBSCOhost), and Scopus. Gray literature will also be searched. Articles will be limited to the English and French languages, commencing from the year 2000. The reference lists of all included articles will be screened for additional articles. Data will then be extracted from included articles and the results will be presented in a table.
Environmental Scan of Existing Digital Health Solutions for Older Adults Living with Neurocognitive Disorders (Mild and Major) and Their Informal Caregivers: Summary Report
Ambily Jose
Maxime Sasseville
Ellen Gorus
Anik Giguère
Anne Bourbonnais
Ronald Buyl
Marie-Pierre Gagnon
: Digital health has added numerous promising solutions to enhance the health and wellness of people living with dementia and other cognitiv… (voir plus)e problems and their informal caregivers. This work aims to summarize 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. We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches for 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. We identified 100 available digital health solutions. The majority (56%) were not specific to dementia. 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 mobility aid tools, pill dispensers, apps, web, or a combination of these platforms. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate eHealth solutions according to their needs and preferences, based on trustable information.