Introduction: Where AI Advances, Responsibility Must Lead

Artificial intelligence (AI) is rapidly becoming a central force in Canada’s healthcare landscape. From hospital triage and diagnostic interpretation to virtual care and administrative streamlining, AI tools are gaining traction across public and private systems. According to the Government of Canada’s 2025 Watch List on Artificial Intelligence in Health Care, more than 30 AI initiatives are underway across provinces, many focused on clinical decision support, risk prediction and system efficiency (Canadian Journal of Health Technologies).

AI’s potential is undeniable: it can accelerate patient assessments, reduce administrative burden and support better health outcomes. But its deployment in real-world healthcare settings is not without risk. The rush to implement AI without rigorous testing, transparency or oversight can lead to misdiagnosis, inequity and eroded public trust.

Unlike other industries, mistakes in healthcare AI are not theoretical—they affect real people. And as a growing number of Canadian healthcare organizations explore or adopt AI-powered solutions, they must also contend with its limitations and consequences.

This blog examines five key risks associated with AI in Canadian healthcare, each grounded in recent research, national policy discussions and provider experiences. It also highlights how Serefin Health upholds care quality by ensuring AI remains human-guided, transparent and ethical.

1. Privacy at Risk in Canada’s Healthcare Landscape

AI systems require large volumes of sensitive data, including patient history, imaging, lab results and behavioural indicators. But this dependence introduces new privacy vulnerabilities, such as:

• In 2023, Canadian hospitals and related vendors reported over 700 healthcare data breaches, exposing more than 50 million records. Many were linked to ransomware or third-party systems (Digitales, 2024).

• A notable breach involving the remote-care platform Aetonix exposed personal health data for over 100,000 patients across 32 clinics in Ontario (IPC Ontario, 2023).
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• Although Canadian privacy laws like PIPEDA and PHIPA mandate informed consent and responsible data use, many AI tools operate in grey zones, where communication about data handling remains limited (TrueVault, 2024).

• According to a 2024 survey by the Medical Council of Canada and the Canadian Dental Association, only 21% of physicians said they trust that AI can properly safeguard patient confidentiality (BusinessWire, 2024).

Privacy is the cornerstone of public trust. In a system already facing scrutiny, breaches and unclear consent protocols can undermine public willingness to engage with digital health tools, delaying progress and jeopardizing safety.

2. Biased AI Can Widen Gaps in Canadian Healthcare

Canada’s diverse population includes Indigenous communities, immigrants, racialized groups, and people living in rural or remote areas. When AI tools are built on datasets that underrepresent these communities, they risk compounding health disparities, such as:

• The Canadian Internet Policy and Public Interest Clinic (CIPPIC), alongside the Brookings Institution, emphasized that biased training data continues to yield worse outcomes for racialized and rural populations (Brookings, 2024).
• A rapid review by Northern Ontario School of Medicine noted that AI tools used in Indigenous and remote communities often struggle to adapt to local health needs and social contexts (ResearchGate, 2024).

• A 2024 study in Frontiers in Public Health found a lack of representative data and model transparency in Canadian public health AI applications, raising concerns about systemic bias (Frontiers, 2024).

Without deliberate inclusion and testing, AI can mirror and magnify existing inequities. The consequences can include delayed diagnoses, poor care alignment and fractured trust, especially in underserved communities.

3. Automation Bias: When AI Weakens Clinical Judgment

One of AI’s strengths is its ability to offer rapid diagnostic or treatment suggestions. But over-reliance on these suggestions can introduce what researchers call "automation bias"—the tendency to defer to machine output, even when it's incorrect. Such cases include:

• Another Canadian study found that pathologists changed correct tumour assessments in 7% of cases based on inaccurate AI output (Research Gate, 2024).

• A 2024 study on AI-assisted wound assessment showed that non-specialist clinicians deferred to flawed AI judgments more often than expected, even when human expertise was more accurate (Research Gate, 2024).
• In a 2023 study published in Journal of the American Medical Informatics Association, clinicians interpreting MRI for ACL injuries showed automation bias in 45.5% of errors when assisted by AI. While AI improved overall accuracy from 87.2% to 96.4%, nearly half of the remaining mistakes were direct results of overreliance on incorrect AI suggestions (National Library of Medicine, 2023).

Why this matters:

Clinical expertise must lead. When AI undermines human judgment, the risk of inappropriate treatment increases. Training, auditability and explainability are essential to maintaining the right balance.

4. Empathy: A Missing Ingredient AI Cannot Replicate

Healthcare is deeply rooted in human connection—trust, emotional attunement and cultural resonance are as critical as clinical accuracy. AI-driven tools, while capable of emulating empathetic language, lack the depth of real empathy and fail to nurture the therapeutic bond crucial in complex or emotionally fraught care settings.

• A 2025 analysis published in the New Yorker highlighted experiments comparing ChatGPT responses to actual oncologists’ answers. When judged blindly on empathy, ChatGPT reply rates surpassed doctors, but critics warned such tests ignore deeper relational, embodied context vital in real care (The New Yorker, 2025).
• A 2024 study on digital clinical empathy using live online chat for cancer support revealed that while AI-suggested empathic phrasing can help, authentic connection requires ongoing adjustment and cultural cues only a trained human can provide (National Library of Medicine, 2024).
• A 2025 study found that while participants rated AI chatbot stories as somewhat empathic, they still significantly preferred human-written narratives—across hundreds of comparisons, human authors consistently outperformed AI in empathy, even when their involvement was anonymous (Multidisciplinary Digital Publishing Institute, 2025).

Empathy is more than kind phrasing—it builds trust, motivates patient engagement, and supports mental well-being. AI may mimic empathy in isolated tasks, but only humans provide the ongoing emotional attunement needed in complex healing journeys. Without genuine care, even the smartest AI can leave patients feeling unseen and misunderstood.

5. Black-Box AI Undermines Accountability

The most powerful AI systems are often the least transparent. These "black box" models generate outputs without clear reasoning, making them hard to validate, audit or contest. Recent examples of this include:

• Although Canada’s Artificial Intelligence and Data Act (AIDA) became law in 2023 to improve oversight of high-impact AI systems, many of its accountability mechanisms—such as transparency standards, audit requirements, and the role of the AI and Data Commissioner—are still in development, leaving healthcare applications without clear regulatory guidance (Government of Canada, 2025).
• In the Centre for International Innovation Governance (CIGI) working paper “Exploring the Future of Generative AI in Public Health Practice”, Melissa MacKay highlights that transparency, accountability and explicit governance protocols remain critical gaps in the implementation of AI in Canadian public health systems (CIGI, 2024).
• According to Vector Institute's December 2024 report "Enabling Trustworthy and Safe AI in Health Care," many AI algorithms used in Canadian healthcare are opaque “black boxes,” making it difficult for clinicians to trace how decisions are made. The report warns that such lack of transparency “diminishes trust in AI systems and can be an impediment to the validation and verification of AI outputs in clinical settings” (Vector Institute, 2024).

Informed consent and trust require clarity. Without the ability to explain AI decisions, patients and providers are left in the dark. This undermines safety, autonomy and legal compliance.

Serefin Health: Strengthening Care with Human-led AI

At Serefin Health, we believe technology should never come at the cost of compassion, safety or clarity. As AI becomes more integrated into healthcare, our commitment remains firmly rooted in person-centred care. That means using AI tools to enhance clinical decision-making, not to replace human insight or connection. In practice, this requires stringent oversight, inclusive design, and a relentless focus on the lived experience of patients and providers alike.

To ensure that AI works in service of people, not the other way around, we have embedded the following safeguards across our care coordination model:

• Human-in-the-loop: Every AI-generated suggestion is reviewed and contextualized by a trained care coordinator.

• Rigorous validation: Tools undergo clinical testing, stress simulations and fairness audits before implementation.

• Ongoing oversight: Real-time dashboards and audit trails help us track AI performance and flag anomalies before they affect care.

• Informed consent: Patients are clearly informed when AI tools are involved in their care and can opt out at any time.

• Bias assessments: Models are continuously evaluated for fairness across age, race, gender, geography and health status.

• Cultural attunement: Our team is trained to provide responsive, culturally safe care that technology alone cannot deliver.

“AI supports decisions. Humans guide care. That is Serefin’s unwavering commitment.”
—Serefin Health Coordination Team

We don’t believe in blind adoption of new technology. Instead, we believe in thoughtful, transparent and empathetic integration that reflects the values of public healthcare: accessibility, equity and trust. By keeping people at the centre of our innovation, we ensure that AI remains a tool for good; one that amplifies the strengths of human caregivers, rather than replacing them.

To learn more about how Serefin balances innovation with integrity, explore our companion piece: Seven Smart Ways to Use AI in Healthcare—Without Losing the Human Touch.

Conclusion: Responsible Innovation Starts with People

AI is transforming healthcare across Canada, but technology alone is not progress. Without human oversight, cultural awareness and legal safeguards, even the most sophisticated systems can fall short.

Every healthcare leader, policymaker and vendor should ask:

• Are patients fully informed and consenting?

• Are privacy and equity built into every layer of design?

• Are decisions explainable and reversible?

• Is AI supporting or overriding professional judgment?

At Serefin Health, our answer starts with people. Every patient interaction is rooted in trust, every tool deployed with care, and every innovation monitored for impact. Because no matter how far AI advances, quality care begins and ends with human connection.