AI can turn a plain-language description into a working software prototype, but in healthcare the hard question is not only whether a tool can be built. It is whether its purpose, data handling, security responsibilities, and clinical role have been checked before anyone relies on it.
That is the argument Dr. Ryan Hungate, an orthodontist and Chief Strategy and Clinical Officer at Henry Schein One, makes in his October 1, 2026 opinion article for The AI Journal. His examples make dentistry an early setting to consider—not proof that AI-built tools are already widely deployed or safe for clinical use.
What “vibe coding” means in healthcare
Vibe coding describes a conversational way to create software: a person explains the outcome they want in natural language, an AI system generates code or an application, and the person iterates by asking for changes. The approach can make an initial prototype more accessible to someone who understands a workflow but does not write software.
Hungate’s thesis is that this could shorten the distance between a healthcare worker’s understanding of a problem and a first software solution. He points to possible dental-office uses such as claims dashboards, scheduling, and reporting. These are examples in an opinion article; it does not document independent deployments, measured benefits, or adoption rates.
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The distinction matters. Making a prototype is not the same as making a dependable operational system. A generated interface may appear useful while still mishandling data, omitting safeguards, or producing results that users misunderstand.
Why healthcare is a tougher test than a startup demo
Healthcare combines sensitive information with workflows where mistakes can affect care. The stakes depend on what the tool is intended to do—not on whether it was written by a developer or generated through conversation.
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| Tool role | Example | What needs scrutiny |
|---|---|---|
| Administrative workflow | Scheduling or a reporting view | Whether it uses electronic protected health information (ePHI), where that information is processed or stored, who can access it, and whether its outputs are accurate enough for the operational task. |
| Clinical-facing workflow | Clinical documentation or decision support | All relevant data and security questions, plus validation appropriate to the intended clinical role and clear human review before real-world use. |
An administrative label does not automatically make a tool low-risk: a report can still expose ePHI or influence consequential decisions. Conversely, the fact that a tool uses AI does not by itself establish that it is a clinical device or that it is unsuitable. Its actual function and data flows are the starting points for assessment.
Hungate argues that clinical-facing tools still need validation. The AI generation method does not remove that assurance burden. Nor does a convincing demonstration establish that a tool is reliable across users, cases, or changing conditions.
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When a cloud service handles ePHI
Under guidance from the U.S. Department of Health and Human Services (HHS) Office for Civil Rights, a covered entity or business associate may use cloud services to process or store ePHI if it has a HIPAA-compliant business associate agreement (BAA) with a cloud provider acting as a business associate and otherwise complies with HIPAA. HHS says a provider that creates, receives, maintains, or transmits ePHI for a regulated entity is generally a business associate—even if it stores encrypted information and cannot decrypt it. See the HHS cloud-computing guidance.
That means “the data is encrypted” is not a complete answer to whether a service relationship is covered by HIPAA. A practice needs to understand what the service actually does with the data and what the agreement says. HHS also does not endorse, certify, or recommend specific cloud products, so a vendor’s marketing language should not be mistaken for a government certification.
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Encryption is one safeguard, not the whole security program
HHS explains that encryption alone cannot ensure the integrity or availability of ePHI, and it does not replace applicable administrative or physical safeguards. A tool may protect data in one state yet still have gaps involving access, changes to records, service outages, recovery, or operational procedures.
Security responsibilities can be divided between a healthcare organization and its cloud provider, depending on the service, risk analysis, and agreements. The organization should identify who implements each applicable safeguard rather than assuming the provider handles everything. HHS’s Security Rule FAQ on small providers says measures may reasonably and appropriately account for an entity’s size, capabilities, and costs; it does not eliminate the need to assess risks and vulnerabilities.
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Questions to answer before a prototype handles real work
Before moving an AI-built tool from experimentation into an operational workflow, a practice or other regulated organization should be able to answer questions like these:
- Data: Does the tool create, receive, maintain, or transmit ePHI? Where is the information processed and stored?
- Access: Which people and services can access the data, and what access controls apply?
- Service terms: If an external provider is involved, what do the service terms and BAA specify about access, retention, disclosure, availability, recovery, and returning data?
- Security ownership: Which safeguards does the practice implement, and which does the provider implement under this particular service configuration?
- Risk management: Has the organization assessed risks and vulnerabilities and established appropriate risk-management measures?
- Clinical assurance: If the tool touches clinical documentation or decision support, how is it validated for its intended use, and what human review occurs before clinical reliance?
These are governance questions, not a checklist that guarantees compliance. The relevant answers depend on the tool’s function, the data involved, the service configuration, and the organization’s obligations. The cited HHS guidance establishes no finding that any particular vibe-coding platform satisfies them.
What healthcare can test that a startup demo cannot
Healthcare is a demanding test of whether easier software creation can be paired with appropriate accountability. A workflow expert may be able to shape an initial tool directly, but practical use still depends on the quality of the tool, the sensitivity of its data, its effect on decisions, and the safeguards around it.
Hungate captures his view this way: “The platforms that win this category won’t be the ones that lowered the barrier the most aggressively.” The point is not that lowering the barrier has no value; it is that accessibility alone is an incomplete measure of success. In healthcare, usefulness has to be matched by trustworthy handling of information and assurance suited to the tool’s role.
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