There is not enough evidence to say that most medical AI platforms fail patient privacy—or to certify ten others as safe. A platform’s privacy risk depends on the particular product, the information it handles, its configuration, and the services that process data behind the scenes. For U.S. healthcare organizations, a Business Associate Agreement (BAA) can be essential when a vendor handles protected health information (PHI), but signing one does not by itself prove that an AI workflow is private, secure, or compliant.
What can—and can’t—be concluded about medical AI privacy
The available official guidance does not establish what proportion of medical AI platforms fail privacy requirements, nor does it provide a representative list of ten platforms that meet a defined privacy standard. A “safe” label without a specified product, deployment, and standard would overstate what the evidence shows.
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The more useful question is whether a specific AI workflow is appropriately governed for the information it receives. That requires looking at the vendor’s role, contract, product scope, settings, downstream services, and safeguards—not just its marketing claims or whether it offers a BAA. The discussion here is U.S.-focused and is not a market-wide audit, legal opinion, or independent security assessment of any vendor.
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When can an AI vendor be a HIPAA business associate?
Under HHS Office for Civil Rights guidance, a vendor’s role depends on what it does with PHI, not simply on whether it calls itself a technology company. HHS gives the example of a third-party AI chatbot on a provider’s patient portal: if it performs services involving PHI, such as symptom assessment, appointment reminders, or scheduling, it may be a business associate.
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That distinction matters because a vendor acting as a business associate may have HIPAA obligations for the work it performs on behalf of a covered entity. An organization should determine the vendor’s role for the actual workflow rather than assume either that every technology provider is a business associate or that an AI vendor is outside HIPAA.
What a BAA does—and what it does not do
A BAA sets contractual requirements for a business associate’s handling of PHI. It is an important part of governing a covered workflow, but it is not a certification that every feature in a vendor’s product is private or that a customer’s implementation complies with HIPAA.
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HHS says the HIPAA Rules do not endorse or require particular technologies. Covered entities and business associates must instead analyze risks and implement reasonable and appropriate safeguards. In practice, a signed BAA has to line up with the product, functionality, configuration, and data flow actually in use. A contract cannot make an uncovered feature or an unreviewed connected service safe by itself.
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HHS says a business associate must have a BAA with a subcontractor before disclosing PHI to that subcontractor to perform work on behalf of a covered entity. Ask which subprocessors, models, connected tools, support systems, analytics services, and logging components can receive or process the information. Review whether the contractual coverage extends to each relevant service; a vendor’s primary platform is not necessarily the whole processing chain.
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Why product scope and settings matter
“The vendor supports a BAA” is not the same claim as “this feature and this deployment are covered by the BAA.” Check the exact product and functionality named in current documentation. Include features such as memory, support, telemetry, integrations, and exports in the review where relevant; they may have different handling or coverage from the core product.
For example, OpenAI’s January 8, 2026 healthcare documentation describes BAA support and states: “Content shared with ChatGPT for Healthcare is not used to train models.” That is a vendor statement about the named product, not independent proof about every deployment or a statement about every OpenAI product. OpenAI also says its BAA applies only to listed eligible products and functionality, and that improved memory is not covered. Organizations should verify current eligibility and configuration for the workflow they intend to use rather than generalize from the product name.
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Public AI tools are not interchangeable with governed healthcare deployments
CMS guidance for CMS employees, contractors, and organizations or individuals working on CMS’s behalf says: “Never disclose or input PII, PHI, or any sensitive CMS data (including financial, health, vendor, procurement, evaluations, draft policies, or proprietary/business information) into publicly accessible AI platforms, chatbots, or prompts.” CMS last reviewed this guidance on August 26, 2025.
This is a restriction scoped to CMS work and the people covered by that guidance. It is not a universal ruling on every private healthcare AI deployment. It does, however, make the practical distinction clear: a publicly accessible chatbot should not be treated as equivalent to an organization’s reviewed, contractually governed workflow.
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How to evaluate an AI workflow that may handle PHI
Use these questions with the vendor and your organization’s privacy and security teams before information enters the system. They are an evaluation framework derived from official guidance and vendor documentation, not a complete legal checklist issued by HHS.
- Map the information. Identify exactly what a user enters or uploads, what the system generates, and which models, logs, analytics tools, integrations, support processes, and subprocessors may receive or process it.
- Determine the vendor’s role. For this workflow, does the vendor perform a service involving PHI on behalf of a covered entity? If so, confirm that the required BAA is signed before PHI is disclosed.
- Check contract coverage at feature level. Confirm that the BAA covers the exact product and functions in use, including relevant connected services and subprocessors. Ask specifically about features such as memory, support, and telemetry rather than assuming they follow the core product’s terms.
- Establish data-use and lifecycle terms. Ask whether customer content is used to train models, what retention and deletion rules apply, and how data can be exported. Get answers for the actual product and configuration, not a general platform statement.
- Review safeguards and accountability. Ask what access controls, audit records, encryption, incident reporting, and risk-management measures apply to the workflow. Confirm who reviews access and how issues are escalated.
- Set limits on use of outputs. Decide how qualified staff check AI-generated output and which decisions remain under clinician oversight. A privacy review does not establish that an output is clinically appropriate.
Don’t overlook tracking and connected services
Data can move through services beyond the visible AI interface. Review tracking technologies and other connected services as part of the data-flow assessment, especially where a site or application handles information tied to a patient interaction. HHS OCR has guidance on online tracking technologies used by HIPAA covered entities and business associates; the page also notes that a court vacated part of the earlier guidance concerning unauthenticated public webpages. Because that interpretation is not settled as described there, avoid treating it as a blanket rule for every public page. Assess the specific data flow and current legal guidance with qualified counsel.
How to compare platforms without calling one “safe”
If you are evaluating more than one option, compare the same documented facts for each exact product and deployment. A vendor’s broad privacy page or willingness to sign a BAA is not enough to demonstrate that a customer’s implementation is compliant or secure.
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- Which subprocessors and connected services can handle PHI, and what contractual coverage applies?
- What are the documented rules for training use, retention, deletion, and data residency?
- What identity and access controls, audit logging, encryption, and incident-reporting measures apply?
- What independent assurance evidence is available, and how current is it?
- What is the intended use, how is the system integrated into clinical work, and what configuration burden falls on the organization?
Record answers against the specific deployment and date reviewed. If evidence is missing or ambiguous, treat that as an unresolved diligence question—not proof that the platform is either safe or unsafe.
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