Bibliographic record
Abstract
What Is the 2025 Watch List? The Watch List is an annual Horizon Scan report from Canada’s Drug Agency that presents emerging technologies and issues that have potential to shape the future of health care in Canada. The 2025 Watch List focuses on the use of artificial intelligence (AI) technologies in health care and the issues that may arise with the implementation of these technologies. AI technologies have the potential to significantly transform health care systems. These technologies could increase efficiency by reducing administrative burden, improve patient outcomes, and enhance patient experience by creating more access points to the health care system. However, there are also legal, ethical, environmental, and social implications with the rollout of these technologies. Why Is This an Issue? Substantial public and private investments are being made in AI technologies for health care. AI technologies are already being implemented in some parts of the Canadian health care system. Commercial options, such as ChatGPT, allow AI technologies to be used by patients to assist with their health care journeys. Because they are readily available and easy to use, these same tools are sometimes used by clinicians and, in some cases, without sanction or training from employers or regulators. AI health care technologies also present an opportunity to fundamentally change health care by its ability to replace, displace, or augment tasks that have traditionally required human cognition. The potential health human resources impact of machines taking on some this load is significant given the increasing demand for health care services and the finite capacity of health care systems in Canada. What Is the Potential Impact? The Watch List signals which technologies are poised to make an impact and the policies, regulatory or organizational enablers, and/or guardrails that are needed to optimize the proliferation of these technologies in the health care system. The 2025 Watch List also focuses on considerations for optimizing and accelerating implementation, such as the massive potential impact on operations, clinical outcomes, and staff and patient experience, while minimizing risks. What Else Do We Need to Know? The 2025 Watch List of AI technologies and issues in health care was developed through consensus-based decision-making at a workshop in November 2024 including individuals from across Canada with experience and expertise in AI. The 2025 Watch List identifies and describes the top 5 new and emerging AI technologies in health care. Examples include AI for notetaking and AI for disease detection and diagnosis. We also explore some considerations for health care decision-makers about the impact these technologies may have on health human resources, health care infrastructure, and health equity. The 2025 Watch List also identifies the top 5 issues related to AI technologies in health care. Examples include the importance of establishing guidelines around what data are used to train AI algorithms and how that might contribute to bias as well as considerations about the liability and accountability of health care providers and systems that use these technologies. These are key issues that warrant more attention and will influence the wider adoption, diffusion, and implementation of new and emerging AI technologies. Monitoring ongoing developments and evidence related to the top technologies and issues highlighted in the 2025 Watch List can help guide health system planning in Canada and improve access to high-quality care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.135 | 0.078 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".