Refining Canadian Healthcare: Unveiling Key Characteristics of AI Algorithms

This article presents a comprehensive protocol for a scoping review focused on artificial intelligence (AI) algorithms developed by researchers affiliated with Canadian institutions. The protocol seeks to identify the essential characteristics of these algorithms that could serve as key leverage points to improve patient care, triage, diagnosis, and overall clinical management in Canada.

Overview

AI—and its ability to perform human-like cognitive tasks such as learning, reasoning, and interaction—has generated significant enthusiasm across various sectors, especially healthcare. With the exponential growth of data and advanced computing power, AI holds the promise of reducing complications, preventing hospitalizations, lightening administrative burdens, and enhancing early disease detection. Yet, despite these advancements, its practical impact in healthcare has not met expected standards. This protocol outlines an effort to understand these gaps and identify opportunities for improvement.

Key Objectives and Research Question

The protocol aims to answer the central research question:

What are the characteristics of AI algorithms developed in a Canadian setting, and which of these can be essential leverage points for improving the impact on Canadian healthcare?

The study will examine:

• Population: AI algorithms developed by researchers affiliated with Canadian institutions.
• Concept: Characteristics such as purpose, clinical applications, validation methods, and data sources used.
• Context: The tangible impact these algorithms have on the Canadian healthcare system.

Methodology and Analysis

The scoping review will adhere to the JBI Methodology for Scoping Reviews and will be reported using the PRISMA-ScR guidelines. The review process includes:

• Literature Identification: Searches will be conducted across MEDLINE (PubMed), CINAHL (EBSCO), and Web of Science (Clarivate) using keywords that connect AI, clinical management, and the Canadian context.
• Eligibility Criteria:
• Inclusion: Articles in English or French published after 2014, detailing AI algorithms for patient triage, diagnosis, or care management, with authors affiliated with Canadian institutions.
• Exclusion: Articles that are reviews, commentaries, editorials, abstracts, or those that do not address practical clinical needs.
• Selection Process: The identified studies will be screened through Covidence, where duplicate entries will be removed and articles will be evaluated by multiple reviewers.
• Data Extraction and Synthesis: Using a data extraction tool, relevant information (including data sources, analytical methods, and clinical objectives) will be tabulated and accompanied by a narrative summary.

Strengths and Limitations

The study design offers several strengths:

• Robust Methodology: A thorough search across multiple established databases maximizes the capture of relevant literature.
• Focused Analysis: Targeting a Canadian context permits an in-depth understanding of jurisdiction-specific challenges, though it may limit broader generalizability.
• Opportunity for Future Comparisons: While the focus on Canadian institutional affiliations ensures specificity, it might exclude valuable models developed by non-Canadian researchers or those using overseas data.

However, there are some limitations:

• Narrow Data Sources: Relying solely on academic literature might overlook models already impacting the Canadian market.
• Practical Constraints Unaddressed: The protocol does not cover real-world deployment challenges of AI in clinical settings.

Addressing Healthcare Sector Challenges

The article details a breakdown of the key challenges in implementing AI in healthcare:

Sector Specificity and Ethical Concerns

The inherent complexity and sensitivity of healthcare, along with strict patient privacy rules, demand that AI models used for clinical decision-making are both reliable and explicable. High error tolerance in healthcare further necessitates rigorous data validation to avoid critical mistakes.

Data Quality and Bias

AI models depend heavily on the quality of their training data. Datasets gathered primarily from academic centers risk introducing bias, especially against underrepresented populations. This underscores the importance of diversifying data sources to create more equitable models.

Maturity of AI Tools

While many AI algorithms have been introduced—especially during the recent pandemic—few have reached sufficient maturity to be integrated into everyday clinical practice. This review will explore how the immaturity of these tools limits their overall impact.

Dissemination and Future Implications

Upon completion by November 2025, the review findings will be shared through peer-reviewed journals and international conferences focused on AI and healthcare. A dedicated workshop will also be held to engage patients, researchers, and other stakeholders, aiming to refine recommendations and support future research directions.

Concluding Remarks

The review protocol outlined in this article endeavors to map the landscape of AI algorithms within the Canadian healthcare framework. By systematically identifying the characteristics that either hinder or enhance the impact of these technologies, the study aims to pave the way for more effective clinical implementations and to stimulate further research in the field.

Ethical Considerations

Since the study involves reviewing publicly available literature, ethical approval is not required. Further, the study follows all ethical guidelines concerning data usage and patient privacy.

Supplemental and Reference Material

Additional materials and full references are provided along with the article to support transparency and reproducibility of the research.