How to evaluate healthcare AI companies: the four vendor types, five checks for regulation, BAA, EHR integration and evidence, plus a vendor checklist.
Lists of "top healthcare AI companies" go out of date quickly, and they rarely help with the decision a healthtech or provider team actually faces: which kind of company should we work with, and how do we check that they can deliver safely? This guide groups healthcare AI companies into four categories, explains what each one is good for, and gives a checklist you can use to evaluate any vendor or partner.
Most companies in this market fall into one of four groups. Many organizations end up working with more than one.
| Category | What they offer | Well-known examples | Best fit when |
|---|---|---|---|
| EHR vendors | AI features built into the electronic health record, such as message drafting, summarization and predictive models | Epic, Oracle Health | You want AI inside the EHR you already run, with minimal integration work |
| Point-solution vendors | A focused product for one clinical or operational task, often FDA-authorized when it is a medical device | Viz.ai and Aidoc (imaging triage), Paige (digital pathology), Abridge (ambient documentation) | You have one well-defined problem and a product already exists for it |
| Cloud and AI platforms | Infrastructure, healthcare data services and foundation models that your team builds on | Google Cloud, AWS, Microsoft Azure | You are building your own product and need HIPAA-eligible building blocks |
| Engineering partners | Teams that design, build, integrate and validate custom AI products and features | Specialist healthtech engineering firms | No product fits your workflow, or AI is part of the product you sell |
The examples are there to show what each category looks like. They are not a ranking or an endorsement, and each company's own site is the best source for what it currently offers.
The category you choose follows from three questions.
Is the problem common or specific to you? Common problems such as imaging triage or visit documentation usually have mature products. Problems tied to your own workflow, data or business model usually do not.
Is AI a feature you use or a product you sell? Providers often buy. Healthtech companies that sell software usually need to own the AI inside their product, which means building it themselves or with a partner.
Do you have the in-house skills? Building safely in healthcare takes ML engineering, integration experience (FHIR and HL7 v2), security and regulatory knowledge. If some of that is missing, a partner can fill the gap while your team learns.
For a deeper look at the build route, see our article on AI solutions for healthtech companies.
Whatever the category, the same five checks separate vendors who can operate in healthcare from those who cannot.
If the product diagnoses, detects or treats a condition, it is likely a medical device. Ask for the FDA authorization and confirm that the cleared indication matches your intended use. You can check the FDA's list of AI-enabled medical devices yourself. If the vendor says the product is not a device, ask how it meets the criteria in FDA's Clinical Decision Support software guidance. For products sold in Europe, ask about CE marking under the EU MDR.
Any vendor that creates, receives, stores or transmits protected health information on your behalf is a business associate and must sign a BAA (HHS sample BAA provisions). Ask which of their own subprocessors, including any LLM provider, are covered by a BAA in turn, and where data is stored and for how long. Our guide to HIPAA-compliant AI covers how this works for large language models.
Ask exactly how the product reads and writes clinical data: FHIR APIs, HL7 v2 interfaces, SMART on FHIR launch or a vendor marketplace. Ask for live references running on your EHR, and who handles the interface work. Integration is often the largest hidden cost in an AI project.
Ask for performance results, the populations they were measured on, and how results differ across subgroups. Ask whether the vendor will support a local validation on your data before go-live. For predictive tools delivered through certified health IT, ONC's HTI-1 rule requires source attributes such as intended use and performance information to be available to users (ONC HTI-1 final rule).
Ask how the vendor detects model drift, how often models are updated, how you will be told about changes and how you can switch a feature off. For FDA-authorized devices, ask whether the vendor has an authorized Predetermined Change Control Plan that defines which model changes it can make without a new submission (FDA PCCP guidance).
| Area | Question to ask | Good answer looks like |
|---|---|---|
| Regulatory | Is this a device, and what is it cleared for? | An FDA authorization number with an indication that matches your use, or a clear non-device rationale |
| Privacy | Will you sign a BAA, and who are your subprocessors? | A signed BAA, a named subprocessor list and a stated data retention period |
| Security | What independent assessments do you hold? | A recent third-party report such as SOC 2 Type II or HITRUST, shared under NDA |
| Integration | How do you connect to our EHR? | Named standards, live sites on the same EHR and a defined integration plan |
| Evidence | How was performance measured, and on whom? | Published or shareable results with subgroup analysis, plus support for local validation |
| Operations | How do you monitor and update the model? | Drift monitoring, change notifications and an off switch you control |
| Commercial | Who owns the data and any model improvements? | Contract terms that limit use of your data and spell out ownership |
Accuracy claims without a source, a population or a comparison point.
Reluctance to sign a BAA, or a BAA that excludes the AI components.
"Integrates with any EHR" with no live references on yours.
No answer to who monitors the model after launch.
Marketing that describes a diagnostic function while the vendor says the product is not a device.
An engineering partner is the right choice when you need AI inside your own product, when you must connect several systems that no single vendor covers, or when you want to own the model and the data pipeline. A good partner brings integration and compliance experience, works to your quality system and hands over code and documentation your team can maintain. Ekipa works as this kind of partner; you can see how we approach it on our healthcare engineering page.
Choosing a healthcare AI company starts with choosing the right category: EHR vendor, point solution, platform or engineering partner. After that, the same five checks apply to everyone: regulatory status, BAA coverage, EHR integration, validation evidence and post-launch monitoring. A vendor that answers all five clearly is far more likely to succeed in your environment than one that tops a list.
If you are deciding whether to buy, build or partner, book a consultation with Ekipa's HealthTech engineers.
Four main types: EHR vendors that build AI into the record, point-solution vendors that solve one task, cloud and AI platforms that provide building blocks, and engineering partners that build custom products.
Yes, if it creates, receives, stores or transmits protected health information on behalf of a covered entity or another business associate. The BAA should also cover any subprocessors, including LLM providers.
Search the FDA's AI-enabled medical device list or the 510(k) and De Novo databases for the product, and compare the authorized indication with how you plan to use it.
Buy when the problem is common and a validated product already fits your workflow. Build, alone or with a partner, when the AI is part of the product you sell or your workflow is specific to you.
Integration. Connecting a tool to the EHR, completing security review and validating it on local data often takes longer and costs more than the license itself.
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