With the growing presence of artificial intelligence (AI) in academia and everyday life, researchers now have access to a wide range of generative AI tools that can enhance efficiency across the research process. While these tools offer important opportunities, they also raise critical challenges and considerations.
This two-part workshop introduces participants to the use of AI, particularly generative AI, in health research. The first part provides an overview of how AI can support different stages of the research process, along with key issues related to privacy, bias, accuracy, reproducibility, and intellectual property. It also addresses emerging legislation and evolving guidelines for publishing and grant applications.
Building on these foundations, the second part focuses on the application of AI in knowledge synthesis, including systematic and scoping reviews. In the context of the rapid expansion of scientific literature, participants will explore how AI can support knowledge synthesis while maintaining methodological rigour. The workshop introduces core principles for the responsible use of AI, including appropriateness, accountability, transparency, validation, and compliance with ethical and regulatory standards, drawing on frameworks such as the RAISE Guidelines.
Participants will gain practical insight into how AI can be integrated across different stages of knowledge synthesis, from the perspectives of both researchers conducting reviews and those developing AI tools.
Teaching formatÂ
July 6 to 7 2026
- Morning hybrid session: 8:00 am to 12:00 pm
- Afternoon in-person session: 1:00Â to 4:00 pm
Location: 2001 ºÚÁÏÍø±¬³Ô¹Ï College Avenue, Montreal, QuebecÂ
Learning outcomes
By the end of this workshop, participants will understand:
- Understand the current possible uses of AI at various stages of the knowledge synthesis process and their limitations.
- Describe core principles for the responsible use of AI in knowledge synthesis and explain why these are important to the integrity of knowledge synthesis creation.
- Make informed decisions regarding whether to use AI tools within knowledge synthesis.
- Apply the RAISE Guidelines to a knowledge synthesis project when using or developing AI tools.
- Describe the possible uses of AI tools in their research process.
- Distinguish the challenges of using AI in research.
- Evaluate the benefits and risks of using generative AI tools in their research projects.
- Identify legislation and guidelines that govern the use of AI in research in their context.
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Day 1 - Monday, July 6 |
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| 8:00 am - 8:30 am |
Light breakfast |
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8:30 am - 12:00 pm |
Possible uses of AI in the research process: literature searching, grant requests, data analysis and visualization, writing, publishing Concerns related to the use of AI in research: bias, ethics, environmental concerns, reproducibility, accuracy, and intellectual property. Legislation and guidelines governing the use of AI in research. |
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12:00 pm - 1:00 pm |
Lunch |
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1:00 - 4:00 pm |
Practicum: Using AI across the research process Participants will work in small groups to explore how generative AI tools can be used at different stages of the research process, while critically assessing their outputs. |
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Day 2 - Tuesday, July 7 |
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| 8:00 am - 8:30 am |
Light breakfast |
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8:30 am - 12:00 pm |
AI in Knowledge synthesis (KT): Provides an overview of the current possible uses of AI at various stages of the KT process and their limitations. RAISE guidelines: Provides an overview of the three documents that comprise the RAISE Guidelines, with a focus on synthesist and AI developer roles, and how these have been adopted to date. Core principles of responsible AI use: Provides information about how to consider and ensure appropriateness and accountability, methodological soundness and validation, compliance with ethical, legal, and regulatory standards, transparency, and reproducibility when using AI for knowledge synthesis. |
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12:00 pm - 1:00 pm |
Lunch |
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1:00 - 4:00 pm |
Participants will apply AI tools to key stages of knowledge synthesis and evaluate their use using principles from the RAISE Guidelines. |
Instructors and organizers
Francesca Frati
Assistant Librarian at ºÚÁÏÍø±¬³Ô¹Ï Libraries
Francesca is the liaison librarian for the Faculty of Dental Medicine and Oral Health Sciences. In this capacity, she provides support for teaching, learning, and research for students and faculty across all programs within the Faculty. She has over 20 years of experience in conducting comprehensive searches for Knowledge Synthesis (KS) projects and teaching KS methods, particularly systematic reviews and scoping reviews. Areas of interest include the responsible use of AI in KS, the role of librarians and expert searchers in KS, AI for risk of bias assessment.
Sandy Hervieux
Head Librarian, Nahum Gelber Law Library, ºÚÁÏÍø±¬³Ô¹Ï
Sandy's research focuses on artificial intelligence in academic libraries, information literacy, and reference services, and she is co-editor of The Rise of AI: Implications and Applications of Artificial Intelligence in Academic Libraries (ACRL, 2022). She is currently pursuing a PhD in the School of Information Studies at ºÚÁÏÍø±¬³Ô¹Ï.
Sreenath Madathil, BDS, PhD
Assistant Professor, Faculty of Dental Medicine and Oral Health Sciences, ºÚÁÏÍø±¬³Ô¹Ï
Sreenath is an oral epidemiologist whose work lies at the intersection of epidemiology, data science, and machine learning disciplines. His research interest is in the implementation of advanced statistical models and machine learning algorithms to support the clinical decision making process in oral health care.
In collaboration with machine learning experts at the department of computer engineering, Sreenath Madathil is developing an individualized risk prediction algorithm for head and neck cancer, which is designed to be used in the primary care setting. He envisions the use of data, analytical methods and technology to empower society, leading to an overall improvement in health and health care while maintaining equity.
PhD Oral Health Sciences Trainee, Faculty of Dental Medicine and Oral Health Sciences, ºÚÁÏÍø±¬³Ô¹Ï
Harsimran Singh Kapoor is an internationally trained dentist from India and is currently pursuing doctoral studies at the Faculty of Dental Medicine and Oral Health Sciences at ºÚÁÏÍø±¬³Ô¹Ï. He is a recipient of Fonds de recherche du Québec – Santé (FRQS) doctoral fellowship. His research focuses on epigenetic age estimation measures, inter-clock variability, and the role of allostatic load in oral health outcomes. Alongside Sreenath Madathil, he co-developed ConvAltumage, a convolutional neural network-based epigenetic clock designed to estimate biological age from DNA methylation data.
He has also been active in student leadership, having served as Councillor-at-Large for the CADR-NCOHR National Student Research Group (2024–2025) and as North America student/trainee representative for the IADR Behavioral, Epidemiologic, and Health Services Research Group (2024–2025). He is also a member of the Oral Cancer Working Group in NCOHR.
Mridul Sharma, BDS, MTech
PhD Oral Health Sciences Trainee, Faculty of Dental Medicine and Oral Health Sciences, ºÚÁÏÍø±¬³Ô¹Ï
Mridul's research sits at the intersection of artificial intelligence and oral health, exploring how advanced AI tools can transform the way dental and oral diseases are diagnosed and managed. He works on building AI systems that go beyond simple predictions, developing models that can reason under uncertainty, communicate their confidence levels, and support clinicians in making safer and more informed decisions.