The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The five technologies below are categories that Canada’s Drug Agency (CDA-AMC) named in its 2025 watch list of artificial intelligence technologies likely to affect Canadian health systems over the next five years. The list is not ranked, and it is not a global top five of health innovations. Each category is explained below with what it does, where it is limited, and what to check before relying on it.
What the list is, and what it is not
CDA-AMC describes its list as a set of emerging issues rather than a ranking of importance. The five categories are AI for clinical notetaking, AI tools for clinical training and education, AI for disease detection and diagnosis, AI for disease treatment, and AI for remote monitoring. Because the list is specific to Canada and to a 2025 publication date, the categories should be read as signals for Canadian health system planning, not as a verdict on which health technologies matter most anywhere else.
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The five categories
1. AI for clinical notetaking
These tools use speech recognition and natural-language processing to transcribe a conversation between a clinician and a patient, then generate a draft clinical note. According to CDA-AMC, the health professional reviews, edits, and signs the output. The same report cautions that AI scribes can produce errors or omissions, so the clinician’s review is part of the safe use of the tool rather than an optional step. Time savings are plausible, but they depend on the clinical workflow and on evidence from the setting where the tool is used. They are not a guaranteed result.
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2. AI for clinical training and education
This category covers tools intended to support learning and practice for clinicians and trainees. Its intended role is to supplement professional instruction, not to replace it or to take over competency assessment. CDA-AMC establishes the category as a policy concern. It does not endorse a particular named tool or make a claim that one approach teaches better than another.
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3. AI for disease detection and diagnosis
The U.S. Food and Drug Administration (FDA) gives examples of AI-enabled medical devices in this area: systems that detect diabetic retinopathy from retinal images, imaging software that sharpens images, and systems that provide diagnostic information for skin cancer. Each example is a specific product with a specific intended use. None of them means that every AI product that touches diagnosis is a medical device, and none means that an output is definitive without clinical context.
4. AI for disease treatment
FDA lists algorithms that automate insulin dosing from continuous glucose monitor readings as an example of an AI-enabled medical device. This is a regulated system with a defined purpose and a defined patient group. It should be kept separate from a general-purpose AI chatbot, which may discuss treatment but is not designed or authorized to direct it. FDA says AI-enabled devices may be reviewed through applicable pathways such as 510(k), De Novo, or premarket approval.
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5. AI for remote monitoring
Remote monitoring extends health-related data collection beyond the clinic. FDA defines digital health technologies broadly, to include computing platforms, connectivity, software, and sensors. It describes studies into how much smartphone- and smartwatch-based wearable sensors and actigraphy measurements vary. The National Institutes of Health (NIH) names wearables and telehealth among digital health areas and says evaluation should span research, community, and clinical settings and different populations. A measurement from a consumer wearable is therefore not automatically clinically validated, and it should not be treated as a diagnosis.
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For readers who want a physical example of wellness tracking, a generic smartwatch health tracker illustrates the sensing described above. It is a wellness tool, not a medical device, and it should not be used to diagnose or manage a condition unless a clinician has specified a validated use.
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“AI” is not a regulatory status
FDA states: “The FDA does not regulate AI as such; it regulates medical devices, including AI-enabled medical devices.” In practice, oversight depends on the product’s intended use and technological characteristics. A tool that is marketed as an AI assistant for clinicians may fall outside device oversight, while a tool that makes a diagnostic or treatment claim may not. The label alone does not establish whether a product is authorized.
FDA reports that more than 1,600 AI-enabled medical devices had been authorized for marketing in the United States as of September 2026. This count is U.S.-specific and changes as authorizations are added, so it should be read with that date and geography attached.
Questions to ask before adopting any of the five
The sources do not rank products, but they point to a consistent set of questions that apply across all five categories:
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- Evidence and validation setting. Was it tested in conditions similar to yours?
- Regulatory status. Is the specific claim authorized in your jurisdiction, not merely the product in general?
- Performance across populations. Has it been checked for different ages, sexes, ethnic groups, and care settings, since CDA-AMC flags bias as a concern?
- Privacy and data security. Where is patient data stored, who can access it, and under what governance?
- Human review and accountability. Who signs off on the output, and who is responsible when it is wrong?
- Interoperability and workflow fit. Does it work with existing records and does it reduce or add work?
- Accessibility and support. What local support, connectivity, and maintenance does it require?
- Total implementation burden. Include cost, training time, and environmental footprint, which CDA-AMC lists among implementation issues.
Access beyond well-resourced systems
The World Health Organization (WHO) publishes an overview of noncommunicable diseases that shapes why these technologies matter globally. The figures below are quoted with their publisher, year, and scope.
| Figure | Source and year | What it refers to |
|---|---|---|
| 74% of global deaths | WHO, 2024 (compendium overview) | Attributed to noncommunicable diseases |
| 86% of premature fatalities in resource-constrained regions | WHO, 2024 (compendium overview) | Presented in the overview’s noncommunicable disease context |
| Over 80% of premature noncommunicable disease deaths | WHO, 2024 | Cardiovascular diseases, cancers, chronic respiratory conditions, and diabetes together |
| 21 technologies assessed | WHO, 2024 compendium | Includes commercially available solutions and prototypes |
WHO’s compendium assesses clinical and regulatory aspects alongside health technology management, local production viability, and intellectual property. For health systems outside the high-income setting, these factors often decide whether a technology can be used at all, so a tool that works well in one country may not transfer without local production, support, and adaptation.
What the evidence does and does not establish
The available sources establish the five categories, the regulatory framing, and the cautions that accompany them. They do not establish a definitive top five, which product performs best in a given category, or how well any of these tools works across all clinical contexts. Where a claim about benefit or accuracy is made, check the validation study, the population it included, and the date of the authorization or report.
NIH puts the tension plainly: “The rapidly evolving use of digital and AI technologies in research and health care has brought opportunities and risks.”
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