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The five health technologies usually meant by this title are the AI categories in the 2025 watch list published by Canada’s Drug Agency (CDA-AMC): AI for clinical notetaking, AI for clinical training and education, AI for disease detection and diagnosis, AI for disease treatment, and AI for remote monitoring. The list is not ranked. CDA-AMC describes it as a set of issues likely to affect Canadian health systems over the next five years, so it is not a global top five of health innovations. This article explains each category, what the U.S. Food and Drug Administration (FDA) and the World Health Organization (WHO) add to the picture, and what to check before trusting a product in any of them.
What the list covers and what it does not
The CDA-AMC watch list is a Canadian document with a five-year horizon. Its five categories are a shared reference point for health system planners, not a measure of which innovations matter most worldwide. A different country, a different time frame, or a different definition of “innovative” would produce a different list. Read the five categories as the topics a Canadian health agency expected to shape decisions through roughly 2030, and not as an endorsement of any particular product.
The phrase “innovative technologies” is also broader than the list. The categories are mostly artificial intelligence applications, and two of them (detection and treatment) overlap with medical devices regulated by agencies such as the FDA. Others, such as notetaking and training tools, may sit outside device regulation altogether. Whether a given tool is regulated, and how, depends on what it is intended to do.
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1. AI for clinical notetaking
These tools use speech recognition and natural-language processing to transcribe a conversation between a clinician and a patient and turn it into a draft clinical note. According to CDA-AMC, the health professional reviews, edits, and signs the output. The same report warns that AI scribes can produce errors or omissions, so the clinician’s review is part of the process, not an optional extra. Time savings are plausible, but they depend on how the tool fits into the clinic’s workflow and on evidence from real settings. No guaranteed saving is established.
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2. AI for clinical training and education
This category covers tools intended to support learning and practice for health professionals. Its role is to supplement instruction, not to replace teachers, supervisors, or formal competency assessment. The watch list establishes the category itself. It does not establish that any specific named tool improves learning outcomes compared with other methods, so a claim of that kind should be checked against the product’s own evidence.
3. AI for disease detection and diagnosis
The FDA’s list of AI-enabled medical devices gives three examples that show the range of uses: systems that detect diabetic retinopathy from retinal images, imaging software that sharpens images, and systems that provide diagnostic information for skin cancer. These examples describe particular intended uses. They do not mean that every AI product in healthcare is a medical device, and they do not mean that any single output is definitive without clinical context. A flagged image still needs to be interpreted by a clinician in light of the patient’s history and other findings.
4. AI for disease treatment
The FDA cites algorithms that automate insulin dosing based on continuous glucose monitor readings as an example of an AI-enabled medical device. This is a regulated, intended-use system: it is designed to act on a specific clinical task with defined inputs. It is not the same as a general-purpose AI chatbot that may discuss diabetes but has not been authorized to dose insulin. Keep that distinction clear when a consumer or clinician encounters “AI treatment” claims. The FDA says AI-enabled devices may be reviewed through applicable pathways, including 510(k), De Novo, or premarket approval, depending on the device and its risk.
5. AI for remote monitoring
Remote monitoring extends health-related data collection beyond the clinic. The FDA defines digital health technologies broadly, to include computing platforms, connectivity, software, and sensors. It describes ongoing work on variability in smartphone- and smartwatch-based wearable sensors and in actigraphy, which measures movement over time. A wearable’s reading can vary with the device, the wearer, and the conditions of use, so a consumer measurement should not be treated as clinically validated unless the manufacturer and an authority have shown that it is. NIH names wearables and telehealth among digital health areas and says that evaluation should span research, community, and clinical settings, and different populations. The NIH also states that “the rapidly evolving use of digital and AI technologies in research and health care has brought opportunities and risks.”
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Questions to ask before adopting a tool in each category
The five categories carry different risks, so the questions that matter differ as well. The table below lists the most useful check for each category.
| Category | Key question before adoption | Why it matters |
|---|---|---|
| Clinical notetaking | Does the draft note match what was said, and who reviews it before signing? | CDA-AMC reports errors and omissions; the signed note carries clinical and legal weight. |
| Training and education | What learning outcome has been measured, and against what alternative? | The watch list supports the category, not a specific tool’s effectiveness. |
| Detection and diagnosis | Which regulatory pathway applies, and on which population was the tool validated? | A device authorized for one use or population may not perform the same way for another. |
| Treatment | Is the system authorized for this exact dosing or treatment decision? | General-purpose AI tools are not authorized treatment systems. |
| Remote monitoring | Has the sensor been evaluated under real conditions, and is the measure clinically validated? | FDA identifies sensor variability as an evaluation concern for wearables. |
What regulators say about AI in health products
The FDA’s framing is direct: “The FDA does not regulate AI as such; it regulates medical devices, including AI-enabled medical devices.” Regulation therefore follows the intended use of a product and its technological characteristics. The label “AI” alone does not tell a buyer whether a product is FDA-authorized. Checking that status means looking at the specific product and the specific claim it makes.
The FDA reported that more than 1,600 AI-enabled medical devices were authorized for marketing in the United States as of September 2026. This is a count that changes as authorizations are added, and it applies only to the U.S. market. Canadian and other national authorities maintain their own requirements, so a product authorized in one country is not automatically available or approved elsewhere.
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CDA-AMC identifies implementation issues that apply to every category, not only to notetaking. These are:
- Privacy and data security: health conversations, images, and sensor streams are sensitive, and a tool may move them to servers outside the clinic.
- Accountability: the organization must be clear about who is responsible when an AI output is wrong.
- Data quality and bias: a tool trained or validated on one population may perform less well on another.
- Data governance: rules for storing, sharing, and retiring data need to be set before deployment.
- Environmental costs: the computing needed to run AI systems carries an energy and resource footprint that health system planners need to account for.
Access and local fit: the WHO view
WHO’s 2024 compendium assessed 21 technologies, including commercially available solutions and prototypes. Its approach looks beyond the technology itself. It assesses clinical and regulatory aspects alongside health technology management, the viability of local production, and intellectual property. A tool that works in one health system may be impractical in another if it depends on imported hardware, unsupported software, or licensing terms a local system cannot meet.
WHO’s overview also frames the broader health context for these technologies. The figures below are the ones the sources attribute to WHO and the FDA, with the scope each one carries.
| Figure | What it measures | Publisher and year |
|---|---|---|
| 74% of global deaths | Share attributed to noncommunicable diseases (NCDs), per the compendium overview | World Health Organization, 2024 |
| 86% of premature fatalities | Share in resource-constrained regions, reported in the compendium overview’s NCD context | World Health Organization, 2024 |
| Over 80% of premature NCD-related deaths | Share in which cardiovascular diseases, cancers, chronic respiratory conditions, and diabetes collectively contribute | World Health Organization, 2024 |
| 21 technologies assessed | Number in the 2024 compendium, including commercially available solutions and prototypes | World Health Organization, 2024 |
| Over 1,600 AI-enabled medical devices | Authorized for marketing in the United States; a changing count, valid as of September 2026 | U.S. Food and Drug Administration, 2026 |
How to compare products in these categories
Comparing two products in the same category is more useful than comparing categories with each other. Use the following sequence, which draws on the frameworks used by FDA, CDA-AMC, and WHO:
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- Define the intended use and clinical task. Write down the single decision or task the tool supports. Everything else follows from this.
- Check the quality of evidence and the validation setting. Note where and on which population the tool was tested.
- Confirm regulatory status for the specific claim and country. A product may be authorized for one use and marketed for another.
- Review performance across relevant populations. Ask whether results hold for the patients or users you serve.
- Assess privacy and data security. Identify where data is stored, who can access it, and how it is used for training.
- Set human review and accountability. Decide who reviews the output and who answers for errors.
- Test interoperability and workflow fit. Check whether the tool connects to existing records and whether staff can use it without displacing other duties.
- Consider access, local support, and total cost. Include local production or support needs, training, maintenance, and the cost of running the tool over time.
These steps do not produce a ranking. They give a buyer or clinic leader a consistent way to ask the same questions of each option, which is the most reliable basis for comparing the five categories in practice.
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