Tech-Driven Reform in Canadian Healthcare: Present Impact, Future Pathways, and Ethical Challenges

Author: Yonina Wu

Abstract

Canada’s public healthcare system continues to face well-documented challenges, such as prolonged wait times, inequitable access in remote and underserved communities, and administrative inefficiencies that hinder the delivery of timely, high-quality care. Concurrently, there is a national and global surge in interest around integrating artificial intelligence (AI) and telemedicine to modernize healthcare delivery. Emerging AI applications, such as disease diagnosis, remote monitoring, and virtual hospital care, promise to enhance medical precision, improve patient outreach, and reduce resource burdens.This study employs a comprehensive methodology rooted in secondary document analysis. It integrates data from Canada’s 2025 Watch List on AI in healthcare, policy reports from government and federal agencies, OECD reviews on telemedicine innovation, scholarly meta-analyses on virtual hospital models, and McKinsey’s economic evaluations. The research explores both domestic and international pilot programs to assess efficacy, scalability, and systemic impact.Results reveal that AI-powered tools, such as automated diagnostic assistants and clinical documentation systems, have demonstrated measurable gains in efficiency and accuracy within Canadian pilot sites. Telemedicine, including real-time video consultations and remote patient monitoring, has markedly improved care continuity in underserved regions, particularly when integrated with virtual ward or hospital-at-home programs. However, adoption faces critical obstacles: inconsistent regulation, data privacy vulnerabilities, system fragmentation, infrastructure gaps, and risks of bias in AI algorithms.Innovations in AI and telemedicine hold transformative potential to significantly enhance healthcare access, reduce systemic inequities, and optimize cost-efficiency. Yet, realizing this potential demands strategic reform: building harmonized governance frameworks, investing in digital infrastructure, enhancing provider and citizen tech-literacy, and proactively managing ethical concerns. Without such measures, technology-driven healthcare advancements risk exacerbating existing disparities.

KeywordsAI in healthcare; telemedicine; virtual wards; digital health reform; Canada; health equity; healthcare innovation

1. Introduction

1.1 Background and ContextCanada’s publicly funded healthcare system has long been regarded as a model of universal coverage, yet it faces mounting structural challenges that threaten its sustainability and equity. Physician shortages, particularly in rural and northern regions, have resulted in millions of Canadians lacking consistent access to a family doctor. Wait times for specialist consultations and elective procedures remain among the highest in the OECD. The COVID-19 pandemic further exposed systemic vulnerabilities, including an overburdened acute care system, fragmented health data infrastructure, and delayed care delivery. In response to these persistent issues, policymakers, health professionals, and innovators have increasingly turned to digital health technologies, such as artificial intelligence (AI), telemedicine, virtual care platforms, and remote patient monitoring as potential solutions. Internationally, health systems are embracing these tools to modernize service delivery, improve outcomes, and reduce cost burdens. In Canada, however, integration remains uneven and policy guidance is still emerging. This context underscores the growing urgency to explore how current technological revolutions can be leveraged to reshape the future of Canadian healthcare.

1.2 Problem Statement and RationaleThe healthcare system is at a pivotal moment. Technological advancements in AI and digital infrastructure are rapidly transforming the way care is conceptualized and delivered around the world. Yet, without a coherent national strategy for integrating these innovations, Canada risks falling behind in both efficiency and equity. The urgency lies not only in adopting new technologies but in ensuring that their implementation aligns with the system’s founding values of accessibility, universality, and quality. Understanding the potential and pitfalls of tech-driven reform is therefore critical for informed, forward-looking policy development. Decisions made in this decade will determine whether innovation serves to bridge longstanding gaps or further entrench disparities in care.

1.3 Significance and PurposeThis study aims to examine the implications of current and emerging healthcare technologies for systemic reform in Canada. As healthcare delivery becomes increasingly digital, it is vital to assess the structural transformations underway, anticipate future developments, and evaluate the risks and benefits that accompany them. The findings are relevant not only to policymakers but also to clinicians, system planners, patient advocacy groups, and technology developers. By identifying key enablers and barriers to innovation, this research contributes to the national discourse on how to responsibly and equitably guide the digital evolution of healthcare.

1.4 ObjectivesThis paper is guided by one overarching question:How can Canada leverage emerging technologies such as artificial intelligence and telemedicine to achieve an equitable, efficient, and sustainable transformation of its public healthcare system?

To address this central question, the study is structured around three core objectives:

• To identify and analyze the healthcare reforms currently being facilitated by technological innovation, with a focus on AI-powered tools, telemedicine platforms, and virtual care models already in use within Canada’s healthcare landscape.

• To explore the future trajectory of healthcare transformation as digital technologies continue to evolve—highlighting developments in precision genomics, synthetic data, and AI-driven clinical education that may shape long-term systemic change.

• To assess the ethical, structural, and practical opportunities and challenges that arise from integrating these innovations into a public healthcare framework—particularly in relation to access, regulation, equity, and public trust.

Together, these objectives provide a framework for understanding how digital innovation can serve not just as a technological upgrade, but as a catalyst for systemic reform rooted in Canada’s founding healthcare principles.

1.5 Scope and LimitationsThe scope of this research is primarily focused on Canada’s public healthcare system, with supporting comparisons drawn from other high-income, publicly funded systems such as those in the United Kingdom, Germany, and Australia. The paper concentrates on clinical and structural innovations, including AI-assisted diagnosis, telehealth platforms, virtual hospitals, and digital health infrastructure. While the study draws from peer-reviewed literature and policy analysis, limitations include the evolving nature of healthcare technologies and limited availability of long-term outcome data for newer interventions. Moreover, the analysis is constrained by the diversity of provincial health systems in Canada, which may exhibit varying levels of technological adoption and readiness.

1.6 Theoretical FrameworkThis research is grounded in three interrelated frameworks. First, the Triple Aim Framework, which evaluates health interventions based on their ability to improve patient experience, enhance population health, and reduce per capita healthcare costs. Second, Innovation Diffusion Theory, which offers insight into how new technologies spread within systems and what factors influence their adoption. Third, the study draws from principles of ethical technology evaluation, including considerations of justice, autonomy, transparency, and accountability in healthcare innovation.

1.7 Methodology OverviewThe study employs both qualitative and quantitative approaches using thematic synthesis of current academic literature, government and NGO policy documents, technology impact assessments, and case studies of Canadian and international pilot programs. Sources were selected for relevance, credibility, and recency, ensuring a comprehensive and policy-relevant overview. Through comparative analysis and critical reflection, the paper identifies not only what reforms are happening, but also how, why, and with what systemic consequences.

2. Methodology 

2.1Search StrategyTo conduct a comprehensive and focused literature review, a systematic search strategy was employed across multiple academic and policy-oriented databases. Primary databases included PubMed, Scopus, CINAHL, Google Scholar, and OECD iLibrary, ensuring access to both clinical and public health sources. Supplementary searches were conducted through Health Canada, CIHI (Canadian Institute for Health Information), and WHO Global Health Observatory to obtain relevant policy and implementation documents. Search terms were designed to capture a broad range of literature related to technology-driven reform in healthcare, including but not limited to: "AI in healthcare", "telemedicine policy", "virtual wards", "digital health equity", "Canadian healthcare innovation", and "remote patient monitoring". Boolean operators and filters were used to refine search results by relevance and publication type. The date range was restricted to January 2015 to June 2025, capturing both pre-pandemic innovations and post-pandemic acceleration in digital health implementation.

2.2 Inclusion CriteriaSources were selected based on relevance to the core research objectives. Included materials met one or more of the following criteria:

• Focused on technological reform in healthcare systems, particularly involving AI, telemedicine, remote monitoring, or digital infrastructure.

• Addressed public health or universal healthcare systems, with special consideration given to Canadian and comparable international settings.

• Provided policy analysis, case studies, empirical data, or systematic reviews of technological implementation or outcomes.

• Published by peer-reviewed journals, government agencies, academic institutions, or globally recognized think tanks (e.g., WHO, OECD, McKinsey). Grey literature was selectively included if it offered unique data or insights not available in peer-reviewed channels.

2.3 Data ExtractionFrom each selected source, key information was extracted systematically to align with the study’s guiding questions. Extracted data included:

• Type of technology: such as AI diagnostic tools, clinical decision support systems, telehealth platforms, virtual wards, or mobile health apps.

• Function and role in reform: the intended impact on healthcare access, efficiency, quality, or cost reduction.

• Implementation context: including geographical setting, population served, and public/private sector leadership.

• Challenges reported: such as regulatory barriers, infrastructure limitations, data privacy issues, and equity gaps.

• Outcomes or projections: including documented effectiveness, cost savings, scalability, and long-term system impact.

2.4 Synthesis MethodThe findings were analyzed and synthesized using a thematic approach, structured around the three guiding research questions:

1. What reforms in healthcare are being enabled by technological innovation?

2. What does the projected path of future healthcare transformation look like?

3. What opportunities and challenges arise as healthcare systems integrate new technologies?

Themes such as governance and regulation, equity and access, infrastructure readiness, and clinical integration were used to group and interpret findings across sources. Cross-comparison of Canadian and international examples provided contextual depth and helped identify globally transferable insights.

2.5 Quality AssessmentTo ensure credibility and rigor, all peer-reviewed sources were evaluated based on journal reputation and methodological soundness. Policy documents and grey literature were appraised using criteria adapted from the AACODS checklist—assessing Authority, Accuracy, Coverage, Objectivity, Date, and Significance. Sources were cross-verified for consistency, and preference was given to multi-source triangulation where available. Any limitations in methodology, sample size, or transparency were noted during the synthesis process.

3. Present Impact

Understanding the present impact of AI and telemedicine is essential to evaluating how Canada can leverage technology to transform its healthcare system. By examining concrete examples of reforms already underway, such as AI scribes reducing physician workload or remote diagnostics expanding access in underserved regions. This section provides a foundational view of what digital innovation can realistically achieve within current system constraints. These findings offer critical insight into the immediate benefits and limitations of technological adoption, helping to ground broader discussions about long-term sustainability, equity, and system-wide transformation.

3.1 AI Medical Scribes: Alleviating Administrative Burden

AI-powered “ambient scribes” are transforming clinical workflows across Canada. In a large-scale pilot conducted by OntarioMD, 162 primary care providers integrated AI scribes during their consultations. The results demonstrated a remarkable 69.5% reduction in documentation time, saving physicians over three hours per week of administrative work¹. Doctors of BC pilots also reported an average of 2.7 hours saved per week and 3.4 fewer minutes recorded per appointment, and 97% of participants stated they would recommend the technology ².

Beyond time savings, these systems enhance qualitative aspects of care. Seventy-five percent of participants in Ontario reported a substantial decrease in cognitive workload during consultations, and nearly half noted improved patient interactions due to greater clinician presence . Despite mixed evidence on burnout reduction, rapid documentation improvements and clinician satisfaction supported the scalable rollout of AI scribes through Canada Health Infoway’s national program ³. Collectively, these results indicate that AI scribes are among the most immediately impactful reforms in Canadian clinical practice.

 3.2 Conversational Diagnostic AI (AMIE): Enhancing Decision-Making

The Articulate Medical Intelligence Explorer (AMIE) represents the forefront of conversational diagnostic AI, leveraging large language models (LLMs) to conduct emulated clinical encounters via text-based dialogue. In a randomized, double-blind, OSCE-style study funded by Canadian institutions, AMIE was evaluated against 20 primary care physicians across 149 case scenarios. Specialist physicians rated AMIE superior to physicians on 28 of 32 performance metrics, while patient actors judged it better on 24 of 26 dimensions, including diagnostic accuracy, history-taking, and empathetic communication ⁴.

Recent advances have expanded AMIE’s capabilities to multimodal reasoning, allowing it to process images (e.g., ECGs, wound photos) and documents during consultations. In an updated OSCE involving 105 multimodal scenarios, AMIE outperformed physicians on 7 of 9 multimodal reasoning metrics and maintained superiority on 29 of 32 foundational diagnostic metrics ⁵. These results mark a transformative moment for AI in clinical decision support, demonstrating both breadth and depth of performance in simulated but realistic environments.

Although still experimental, AMIE is setting new benchmarks. Its robust dialogue quality and structured reasoning make it a candidate for augmentation in triage, rural telehealth, and specialist shortage mitigation. Continued validation in real-world settings, particularly with patient-facing interfaces is the next critical step for this revolutionary technology.

3.3 AI-Guided Remote Monitoring and Diagnostic Outreach

AI is enhancing remote patient monitoring (RPM) and tele-diagnostic services within Canadian healthcare systems. While RPM pilots incorporating AI analytics in cancer and chronic care have shown improved patient engagement, evidence of superior outcomes is still developing . However, RPM systems that leverage AI for real-time alerting are now integral to expanding virtual wards and home-based care models, which Canada is prioritizing as part of post-pandemic healthcare transformation.

One notable pilot used AI-guided tele-ultrasound to support ultrasound image acquisition by medical novices on Haida Gwaii (BC), directed remotely from Vancouver, nearly 754 km away. The AI tool successfully ensured acquisition of all required diagnostic images at acceptable quality, signaling a breakthrough for remote prenatal and emergency care .

Canadian health authorities are now embedding AI-enhanced RPM and tele-diagnostics within provincial virtual ward frameworks. These systems offer proactive interventions and reduce hospital readmissions when supported by data-driven monitoring, although full system integration and longitudinal outcome data remain nascent. Nonetheless, the convergence of AI with telehealth infrastructure shows enormous promise for improving care access and early intervention in underserved regions.

3.4 Summary

Canada is witnessing three transformative AI-driven reforms:

• AI scribes have cut documentation time by ~70% and saved multiple hours weekly per physician, enhancing clinician presence and satisfaction.

• Conversational diagnostic AI like AMIE is outperforming doctors in structured evaluations and paving the way for AI-augmented clinical dialogue.

• AI-enhanced remote diagnostics and RPM are evolving telehealth capabilities, bringing advanced clinical services to remote and rural areas.

These findings demonstrate that Canada is already making measurable progress toward answering the broader question of how technology can drive equitable, efficient, and sustainable healthcare reform. The real-world success of AI scribes in alleviating administrative strain, the promising diagnostic capabilities of systems like AMIE, and the expanding reach of AI-enhanced remote monitoring all point to a system beginning to evolve in the right direction. While challenges remain, these present-day reforms offer compelling proof that emerging technologies are not just theoretical tools, they are practical solutions actively reshaping the delivery of care. By continuing to scale and refine these innovations, Canada lays a strong foundation for achieving a more accessible and future-ready public healthcare system.

4. Future Pathways

To fully address how Canada can leverage emerging technologies for a more equitable, efficient, and sustainable healthcare system, it is essential to look beyond present reforms and examine what lies ahead. This section explores the most promising future pathways for digital transformation, specifically, the integration of AI-powered precision genomics, generative AI in clinical training, and synthetic patient data platforms. These innovations not only represent technical progress but signal systemic shifts in how care will be delivered, how health professionals will be trained, and how data will be protected and utilized. Understanding these trajectories helps policymakers anticipate what investments, regulations, and capacity-building efforts are needed to guide digital reform in alignment with Canada's healthcare values.

4.1 Precision Genomics Powered by AI

One of the most transformative future pathways lies in precision health, where AI-enabled genomics will drive individualized care at scale. In March 2025, the federal government announced the Canadian Precision Health Initiative (CPHI)—a $200 million federal investment to sequence over 100,000 genomes, aiming to create a comprehensive, population-representative genomic database ⁶. By building this foundation, Canada positions itself to harness AI for gene-disease association discovery, pharmacogenomic screening, and prognostic modeling.

Embedded within this initiative is the Canadian Platform for Genomics and Precision Health, a federated system used to develop AI-driven models for oncology, rare diseases, infectious disease, and neuroscience ⁷. These architectures allow AI systems to learn from vast, distributed datasets while preserving patient privacy, which is a necessary step for scaling personalized algorithms across the country’s diverse population.

Looking ahead, AI-guided precision medicine is expected to enable "digital twins"—virtual representations of patients based on molecular, clinical, and lifestyle data⁸. Such tools will transform treatment planning, enable predictive interventions, and support adaptive therapies that respond to real-world patient responses, shifting healthcare from reactive treatment to proactive, tailored prevention.

4.2 Generative AI in Clinical Education and Training

Alongside clinical deployment, generative AI is poised to revolutionize medical education and workforce training. Canada’s 2025 AI Watch List identified AI tools to accelerate and optimize clinical training as a core future technology, highlighting how immersive model-driven learning platforms can fill critical gaps in clinical competencies ⁹. These platforms will utilize realistic AI-powered patient simulations to provide scalable, risk-free training environments for learners.

Already, several leading health systems across Canada have begun integrating AI simulators into continuing education programs. By enabling scenario-based training with instant feedback, AI tools can vastly improve learner performance and retention, particularly in remote or under-resourced regions. They also aid in standardizing training outcomes, reducing variability in clinical exposure.

This trajectory suggests a future where generative AI not only supports initial medical training but also becomes a lifelong companion in professional development. AI-based coaching systems that analyze practitioner patterns, benchmark performance, and adapt training content will help maintain high standards of care while reducing educational disparities across provinces.

4.3 Synthetic Healthcare Data for Privacy and Innovation

Another critical future revolution is the adoption of generative AI for synthetic patient data—data that mimics real records without risking privacy. As Canada expands its electronic health infrastructure under Canada Health Infoway ⁷, the challenge of data privacy becomes more pronounced. Synthetic data enables researchers and developers to test algorithms safely, without compromising personal identifiers.

Global evidence indicates that synthetic data generated by GANs and VAEs can maintain statistical fidelity while protecting patient confidentiality ¹⁰. Implementing synthetic data systems enables large-scale AI validation and interoperability testing across jurisdictions, accelerating innovation cycles and reducing deployment delays.

Looking forward, regulated synthetic data platforms may form the backbone of national health AI ecosystems. By allowing secure access to de-identified yet realistic datasets, Canada can foster a climate of innovation while upholding public trust, balancing progress with protections in an increasingly digital health landscape.

4.4 Summary 

Looking ahead, Canada’s healthcare revolution will be shaped by three foundational innovations: AI-powered precision genomics, generative AI in clinical training, and synthetic data platforms. Each signals a major shift in how care is delivered, how providers are trained, and how data is managed, pushing the system toward a more predictive, personalized, and ethically conscious model. These technologies also offer scalable opportunities to address workforce shortages, improve patient outcomes, and protect privacy in an increasingly digital landscape.

To answer the big question of how Canada can achieve equitable and sustainable healthcare reform through technology, this section underscores the importance of acting now. By investing in infrastructure, refining regulatory frameworks, and building capacity for innovation, Canada can prepare its public health system for a future that is not only more efficient, but also inclusive, responsive, and trustworthy.

5. Ethical Challenges

While technological innovation offers powerful tools to improve healthcare, its integration into Canada’s public system must be evaluated not only for performance but for its ethical and structural implications. This section examines both the opportunities and challenges that arise as AI and digital health technologies become more embedded in care delivery. Specifically, it explores how these tools can enhance patient autonomy, equity, and trust, while also highlighting the risks of data misuse, algorithmic bias, and legal ambiguity. Grappling with these dimensions is essential to answering the big question: it is not enough for Canada to adopt emerging technologies, it must do so in ways that uphold justice, accountability, and transparency across the system.

5.1 Opportunities

5.1.1 Empowering Patient Autonomy and Informed Engagement Digital health technologies, including personalized treatment plans, predictive analytics, and patient portals enhance patient autonomy by providing accessible information and decision-making tools. These opportunities align philosophically with the principle of autonomy, enabling individuals to make more informed health decisions. Regulatory tools like the Government of Canada’s Algorithmic Impact Assessment system and “Directive on Automated Decision‑Making” aim to strengthen user consent and transparency, ensuring that AI-augmented health decisions are both understandable and voluntarily accepted by patients¹¹.

5.1.2 Enhancing Justice and Equity Through Data Democratization When responsibly deployed, AI can identify underserved populations and tailor interventions to address inequities, from rural tele-ultrasound to targeted remote monitoring programs. The ethical principle of justice—fair distribution of benefits across societal groups, finds expression in policy frameworks like the Canadian Precision Health Initiative, which seeks to build a representative genomic database for equitable outcomes across all communities . These efforts help ensure that innovation benefits are shared rather than concentrated among traditionally privileged groups.

5.1.3 Strengthening Accountability and Institutional Trust Philosophical notions of stewardship and accountability demand that health systems maintain trustworthiness during transformative technological adoption ¹². Canada’s digital health regulatory landscape, as highlighted in the 2025 ICLG report, emphasizes formal interoperability standards, medical-device coordination, and liability models that hold both providers and developers accountable for AI-integrated care ¹³. This structured oversight encourages transparency, encourages best practices, and builds public confidence in digital health.

5.2 Challenges

5.2.1 Balancing Privacy, Data Sovereignty, and Commercial Interests AI systems in healthcare require vast quantities of patient data, raising legal and ethical concerns, particularly under PIPEDA and provincial privacy acts. Critics argue that these regulations lag behind the speed of technological innovation, exposing Canadian patients to data withdrawal risks or misuse ¹³. Philosophically, this raises questions about delegating moral agency to algorithmic decisions and the right of individuals to control personal health data, a tension between utilitarian benefits and deontological privacy rights.

5.2.2 Navigating Liability and Transparency in “Black-Box” AI Opacity in machine learning, where AI outputs cannot be readily explained, poses legal uncertainty around responsibility when errors occur. Without clear standards for interpretability and physician oversight, liability for AI-aided clinical decisions becomes ambiguous . From a principlist ethical standpoint, this conflicts with the duties of non-maleficence and justice, as unexplainable errors threaten patient safety and trust. Ongoing philosophical debates suggest that principles alone are inadequate for governing machine ethics in healthcare without robust accountability frameworks.

5.2.3 Risk of Entrenching Bias and Exacerbating Health Disparities AI models frequently inherit societal biases reflected in their training data, leading to skewed diagnostic or treatment outcomes for marginalized groups. This undermines the ethical principle of justice, amplifying health inequities rather than mitigating them. Legal recourse is limited by current privacy and anti-discrimination statutes, which often fail to capture algorithmic harms. Ethically, this challenges utilitarian approaches and underscores the need for value-sensitive system design, fairness-aware ML, and inclusive oversight mechanisms ¹⁴.

5.3 Summary

The analysis in this section reinforces that the success of Canada’s healthcare transformation will depend not solely on technological capability, but on the ethical and regulatory scaffolding built around it. Opportunities to enhance autonomy, improve equity, and build institutional trust are tangible and already reflected in current policy initiatives. However, unresolved challenges, ranging from data privacy and liability gaps to the risk of algorithmic discrimination, pose serious threats to the fairness and safety of digital healthcare. These tensions make clear that innovation alone cannot guarantee meaningful reform. If Canada is to truly leverage technology for equitable, efficient, and sustainable change, it must invest just as seriously in governance, legal clarity, and inclusive design as it does in hardware and algorithms. This section ultimately argues that the digital transformation of healthcare will only achieve its promise if it is accompanied by an equally robust transformation in policy, ethics, and public oversight.

6. Conclusion

Canada’s public healthcare system is undergoing rapid transformation through the integration of artificial intelligence, telemedicine, and digital infrastructure. This study has shown that AI scribes reduce administrative burdens by up to 70%, conversational diagnostic tools like AMIE outperform physicians in simulated encounters, and AI-powered remote monitoring extends access to underserved regions. These reforms represent measurable and scalable improvements in care efficiency, diagnostic accuracy, and system capacity, directly addressing Canada’s long-standing issues of access, equity, and quality.

These innovations represent more than technical progress; they signal a paradigm shift in how healthcare can be conceptualized and delivered. On a practical level, AI-enabled tools streamline workflows, enhance diagnostic precision, and expand service delivery to previously neglected populations. On a systemic level, the research demonstrates that technological innovation can reinforce the foundational values of Canada's public healthcare system: universality, equity, and sustainability. When supported by thoughtful integration and ethical oversight, these technologies hold the potential to drive meaningful reform. By documenting Canada’s current trajectory, this study contributes to a growing understanding of how digital transformation can serve as both a technical and value-driven strategy.

This research set out to examine how Canada might leverage emerging technologies to achieve transformative healthcare reform. It met this objective by identifying current reforms facilitated by AI and telemedicine, exploring future directions such as precision genomics and synthetic data, and assessing ethical and policy challenges including bias, data sovereignty, and regulatory gaps. Together, these findings confirm that while digital reform is actively underway, its outcomes remain dependent on how thoughtfully these tools are implemented and governed.

To realize the full potential of digital transformation, Canada should take the following steps:

• Harmonize digital health governance across provinces to ensure consistent regulation and interoperability.

• Invest in infrastructure for rural, remote, and Indigenous communities to bridge digital access gaps.

• Develop national synthetic data platforms to enable innovation while upholding patient privacy and trust.

• Promote digital health literacy among both healthcare providers and patients to support ethical adoption and informed consent.

These actions will help accelerate innovation while safeguarding public trust and equity.

This study is limited by the emerging nature of many healthcare technologies and the lack of longitudinal data from real-world Canadian deployments. Additionally, significant provincial variation in policy, infrastructure, and readiness limits the generalizability of certain findings. Future research should examine long-term outcomes and equity impacts across diverse population groups and settings.

Canada stands at a pivotal moment. With the right investments, policies, and ethical guardrails, emerging technologies can serve as powerful tools for creating a more inclusive, efficient, and future-ready healthcare system. However, innovation alone is not enough. To answer the big question of how Canada can leverage technology to transform its healthcare system, this study concludes that success depends not just on what is adopted but on how it is governed. If guided with care, Canada's digital revolution can become a model for equitable healthcare transformation on a global scale.

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