Do not judge a health technology startup by its most impressive headline, a journal publication, or the words “FDA registered.” Translate each promise into a specific, testable claim, then check whether evidence supports that claim for the product, people, setting, and intended use being promoted. Clinical value, technical accuracy, regulatory status, usability, and cost are connected questions—but none is a substitute for the others.
What does the startup actually claim?
Start by writing down the exact statement you want to assess. A phrase such as “improves health” is too broad to evaluate. A useful claim identifies what changes, for whom, compared with what, and over what period. It should also make clear whether the company is promising diagnosis, prevention, treatment, improved engagement, or lower cost.
Turn a promise into a testable question
For each claim, record the population, care setting, timeframe, comparator, outcome, and claimed magnitude. For example, instead of recording “helps people manage a condition,” ask whether the product improves a specified outcome in a defined group of people, compared with a stated alternative, over a stated period. This is a way to clarify the claim, not evidence that the product achieves it.
- Population: Who is supposed to benefit? Consider whether the claim is for adults, children, people with a particular diagnosis, or another defined group.
- Setting: Is the product intended for independent use at home, use during a clinical visit, or use by a clinician in a care workflow?
- Function: Is it measuring, screening, diagnosing, predicting, recommending, treating, or supporting engagement?
- Outcome: What changes—such as a clinical result, a decision, a symptom, or a cost—and how is that change measured?
- Comparison and timeframe: Compared with usual care, another product, or no intervention? Over how long?
- Magnitude: How large an improvement or reduction is being promised?
Include the impression created by the whole marketing context, not only a slogan. Images, demonstrations, testimonials, charts, and surrounding copy can imply a health benefit even if no single sentence states it directly. FTC staff guidance on health products says that the reasonable basis for a claim is evaluated in context, and that the level of substantiation depends on the product and claim. A health claim with serious consequences if wrong calls for especially careful scrutiny.
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What evidence supports that specific claim?
Ask the startup for the full study or evaluation behind the exact claim—not just a slide, summary, press release, or selected result. Then check whether it tested the product and version actually being marketed, in a population and setting that resemble the intended use, and whether it measured the promised outcome.
Request the materials needed to assess the study
Where applicable, request the study report, protocol, registration, participant characteristics, comparator, endpoints, analysis, limitations, and relevant disclosures. A protocol helps show what the investigators planned to measure before seeing the results; registration can help a reader check whether reported outcomes match the original plan. These materials do not automatically prove a claim, but they make it possible to assess how the result was produced.
Check that the tested product is the same version and configuration being sold. If the product has changed, ask what changed and whether the evidence still applies. A study of an earlier algorithm, sensor, workflow, or user group may not establish performance for a current offering. Also distinguish studies of a general method from studies of the startup’s particular product: evidence that an approach can work in principle is not the same as evidence that this implementation delivers the advertised result.
Prefer outcomes that match the promise
Ask whether a study measured the claimed clinical outcome or a more convenient proxy. A measure such as app use, time spent in the product, or completion of a program can be relevant to engagement, but it does not by itself show improvement in health. Likewise, a technical measurement may be accurate against a reference method without establishing that using it improves a patient’s outcome. The evidence should match the type of benefit being advertised.
For health-benefit claims, FTC staff guidance generally expects randomized, controlled human clinical testing. As the FTC’s 2022 Health Products Compliance Guidance puts it: “As a general matter, substantiation of health-related benefits will need to be in the form of randomized, controlled human clinical testing to meet the competent and reliable scientific standard.” The appropriate design and strength of evidence depend on the claim and the field; the guidance does not mean that any single study design or result automatically settles every question.
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How can you tell whether a study is persuasive?
Study count is a poor shortcut. Assess design, implementation, results, and analysis. FTC guidance identifies these as relevant considerations and emphasizes that quality matters more than simply accumulating studies. A weak or poorly matched study does not become compelling just because several similar studies exist.
Look at the comparison and the participants
Ask whether there was an appropriate control group and whether the comparison isolates the product’s effect. If people using the product also received different care, extra attention, or other support, consider whether those differences could explain the result. Check whether the study population resembles the people the company says it serves. A result in one narrow group may not support a broader claim about different ages, conditions, or settings.
Check follow-up, outcomes, and analysis
Was follow-up long enough to observe the outcome being claimed? Was the outcome meaningful to patients or clinical decisions, or was it an intermediate measure that may not translate into a benefit? Read the conclusion alongside the methods and results. Look for whether the analysis supports the stated conclusion, whether the limitations are clearly described, and whether an independent expert in the relevant field has reviewed the interpretation.
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Sample size and duration should be judged in relation to the hypothesis, study design, outcome, and norms in the relevant field. A large participant count does not repair a mismatched outcome or comparison; a short study may be insufficient for a claim about a lasting effect. The useful question is not whether a study is “big” in the abstract, but whether its design and data can reasonably support the specific conclusion.
Does publication or peer review prove that a product works?
No. Publication makes a study easier to examine; it does not, by itself, establish that the product works. Peer-review rigor varies, and the reader still needs to inspect the methods, participants, outcomes, analysis, and fit with the marketed product. A published paper can be informative without substantiating every claim the company makes.
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Look for independent replication and contrary results
Search for studies conducted by researchers independent of the company, including results that do not support the claim. Independent replication can increase confidence that an earlier result was not caused by an undetected bias or a feature of one study. It is not a guarantee of effectiveness, but it provides a more robust basis for judgment than relying only on company-sponsored claims or one favorable result.
Consider all relevant evidence rather than counting favorable findings. A company’s study may be useful, but readers should ask who conducted and funded it, what was disclosed, and whether other researchers have reached similar conclusions. Public health recommendations can summarize evidence for a population or practice; they do not, on their own, substantiate a product-specific causal claim.
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Not necessarily. FDA status and clinical effectiveness are related but distinct questions. The regulatory pathway or status applicable to a product depends in part on its intended use and software function. A clearance, authorization, approval, registration, listing, or claimed exemption should not be treated as proof of every clinical outcome in the company’s advertising.
Start with intended use and function
Ask what the product is intended to do, who uses it, what inputs it analyzes, and what output it provides. Is the software presenting information, making a recommendation, analyzing a signal or image, or producing an output intended to inform an individual patient’s care? These details matter to regulatory fit. A company’s description of its own product is a starting point, not a substitute for checking the exact product, intended use, and current regulatory record.
FDA’s policy navigator describes criteria for certain clinical decision support (CDS) functions. The statutory CDS exclusion requires that all applicable criteria be met. One important consideration is whether a health professional can independently review the basis for the recommendation, rather than primarily relying on the software for an individual patient’s decision. Functions that analyze medical images or certain signals may not meet the CDS exclusion. The precise regulatory treatment depends on the product’s particular function and intended use.
Do not treat regulatory terms as interchangeable
“FDA-cleared,” “FDA-authorized,” “FDA-approved,” “registered,” and “listed” refer to different things and should not be used as synonyms. For a company-specific conclusion, verify the exact product, its intended use, the relevant FDA decision or record, and whether that information is current. The general criteria in this guide cannot establish the status of any named startup or device, or resolve a particular legal question.
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How do you evaluate a wearable or AI-derived health measurement?
Follow the measurement chain from the physical signal to the reported number and then to the clinical interpretation. A metric is useful only if it is suitable for its proposed purpose. A company should be able to explain what its sensor measures, how raw signals become an output, how performance was validated, and whether the output is meaningful for the use being promoted.
Trace the signal and validation method
- Measurement: What does the sensor directly measure, and what is inferred from that signal?
- Processing: How are raw signals converted into the reported metric or AI-derived output?
- Reference: What reference method was used to judge accuracy or agreement?
- Validation group: In whom was performance evaluated, and does that group resemble the intended users?
- Setting: Was the product tested in a controlled evaluation, a real-world setting, or both?
- Failure modes: What conditions, user actions, or sources of error can make the reading unreliable?
Then ask whether users can follow the instructions and whether clinicians or users can interpret the output appropriately. FDA’s 2026 paper, Key Considerations for the Development and Use of Digitally Derived Measures for Clinical Investigations, says: “Evidence for validation should demonstrate that users understand and can follow the instructions for use.” The paper also emphasizes fit-for-purpose verification and validation, usability, sources of error, patient and clinician input, and risk-based evidence generation.
Accuracy is not one universal property that transfers automatically across purposes. A measurement validated for one use, population, reference method, or setting may not establish suitability for another. The relevant question is whether performance is adequate for the decision or outcome the company says the metric supports, including the consequences of an incorrect result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare two health technology products?
Compare the whole product against the needs of the person or organization that would use it. A 2019 peer-reviewed digital health evaluation article by Mathews and colleagues proposed assessing technical performance, clinical evidence, usability, and cost. These are practical comparison domains, not a universal official scoring system. Regulatory fit is also important when evaluating the product’s intended use and public claims.
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| Domain | Questions to ask | What a stronger case looks like |
|---|---|---|
| Clinical evidence | Does the study design fit the claim? Do participants and settings match? Are outcomes relevant, follow-up adequate, and results independently replicated? Is contrary evidence considered? | Evidence that directly addresses the advertised outcome in a relevant population, with a suitable comparison and transparent limitations. |
| Technical performance | Was the product verified and validated? What accuracy or performance measure was used, against what reference, and in which population? What failure modes are known? | Performance evidence that matches the intended measurement and use, with relevant users and conditions represented. |
| Usability and implementation | Who is expected to use the product? Can they follow the instructions? Does it fit the workflow, and what accessibility or support needs matter? | Evidence that intended users can operate and interpret the product as expected in the setting where it will be used. |
| Regulatory fit | What is the intended use and relevant regulatory status or pathway? Do public claims stay within the evidence and regulatory context? | A clearly identified product and intended use, with status checked against a current record rather than inferred from broad terminology. |
| Cost and value | What is the total cost to the user or buyer? Is a savings or value claim supported by evidence, rather than by engagement or convenience alone? | Costs and claimed savings assessed for the relevant user, buyer, and period, with the basis for any value claim made clear. |
Before comparing products, write down the end user’s requirements. A product that performs well on a technical measure may still be a poor fit if users cannot follow its instructions or if its output does not support the decision at hand. Likewise, convenience or high engagement may matter to a buyer, but neither should be mistaken for evidence of clinical benefit unless the claim and evidence establish that connection.
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What does the evidence say about health startups as a group?
A 2022 JMIR study analyzed 224 digital health companies selling products in prevention, diagnosis, or treatment. Within the study’s defined cohort and scoring method, the reported mean clinical robustness score was 2.5; 98 of 224 companies (44%) scored zero, while 45 (20%) scored five or more. The score was based on clinical trials and regulatory filings identified for that cohort.
The study also reported that its clinical robustness score did not correlate with the number of clinical claims (r²=0.02), total funding (r²=0.08), or company age (r²=0.18). These findings are a reason to evaluate evidence directly rather than infer it from a startup’s visibility, funding, age, or number of promises. They are not a current estimate of the share of all startups with weak evidence: the analysis covered a defined cohort, used a study-specific measure, and reviewed public company websites during 2021. It does not audit every current company or every product now on the market.
What can this evaluation establish—and what remains separate?
This process helps a reader test whether public claims are matched by relevant evidence. It does not settle the legal status or clinical value of a particular company. A specific conclusion requires review of the named product, exact intended use, actual claim, and applicable jurisdiction. FDA and FTC materials can change, so company-specific regulatory and advertising assessments should use current official records and guidance.
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Quick Recap
A practical diligence sequence
- Capture the claim. Save the exact wording and the surrounding context, including any implied promise. Identify the audience and intended user.
- Make it testable. Specify population, setting, function, outcome, timeframe, comparator, and claimed magnitude. Note which part is unclear.
- Request the evidence. Ask for the full study or report, protocol, registration where applicable, participant details, comparator, endpoints, analysis, limitations, and disclosures.
- Match product to evidence. Confirm that the tested version, configuration, users, and setting correspond to the product being promoted.
- Assess study quality. Examine the control, population, follow-up, relevant outcomes, analysis, and whether the evidence supports the stated conclusion.
- Search beyond the company’s materials. Look for independent replication and results that contradict or qualify the claim. Do not treat publication alone as proof.
- Check regulatory fit. Establish intended use and function; distinguish among FDA terms; verify the exact product and current record before drawing a company-specific conclusion.
- Inspect measurement and usability. For digital measures, trace the sensor-to-output chain, reference method, validation population, real-world errors, and user instructions.
- Compare the full value proposition. Assess clinical evidence, technical performance, usability, regulatory fit, and total cost against actual end-user requirements.
- State the conclusion narrowly. Say which claim is supported, by what evidence, for which product and population, and what remains unestablished. Avoid converting a limited finding into a blanket verdict about the company.
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