AI and Patient Information: Traceability and Verifiability
AI is increasingly used in patient communication at dental clinics – often as text suggestions, summaries, translations, or automated responses in digital platforms.
When AI influences how information is formulated, what is sent, or the guidance a patient receives, a question of responsibility arises that is not always clearly defined in the clinic.
This article explains where responsibility and risk typically arise with AI use in patient information and which governance principles should be in place to ensure traceability and verifiability in practice.
What is meant by AI use in dental health?
In patient communication, AI can be both visible and invisible. Some clinics actively use AI to create drafts, while others have AI functions “embedded” in tools for text, email, chat, or case handling.
AI use in patient information can include:
Drafts and Phrasing: suggestions for emails, SMS, letters, or text in patient portals.
Summarization: compression of long explanations into “brief versions,” or conversely expansion of standard text.
Adaptation: simplification of language, adaptation to target group (e.g., children/parents) or situation (aftercare).
Translation: multilingual communication, or simplification to more understandable Norwegian.
Automated Responses: initial response to inquiries, sorting of requests, or suggested responses to staff.
Common to these areas of use is that AI can affect the patient’s understanding, expectations, and actions. This makes patient information an area where quality and control are not just about “good phrasing,” but about verifiable practice.
Where does the responsibility question arise?
The responsibility question typically arises at the intersection of the clinic's professional responsibility and the communication flow that AI contributes to. In patient information, this becomes particularly evident in five situations:
When AI alters the precision of content without it being obvious
Small linguistic adjustments can change meaning: what is recommended, what is optional, what is risk information, or what is time-sensitive. The risk is not necessarily dramatic errors, but a gradual shift in precision over time.When advice and guidance appear authoritative
AI text can seem reliable and consistent. This can provide a false sense of security for both staff and patient. Particularly in aftercare instructions, pain relief, bleeding, signs of infection, or follow-up, the phrasing can have practical consequences.When it becomes unclear who “approved” the communication
If a message is sent with a quick glance, or without a clearly responsible role, it can be difficult to explain afterwards who made the judgment and on what grounds.When patient data becomes part of the text or workflow
Personal data can be incorporated into drafts, compilations, or templates. This raises the need for control over data minimization, access, and ensuring the right information gets to the right recipient – even when much happens quickly in a hectic operation.When the clinic cannot reconstruct what was actually sent
Traceability is not just about “having a copy.” The clinic should be able to answer: What was the original text basis? Was AI used to generate or change content? Who edited? When was it sent? If this cannot be verified, subsequent responsibility clarification is weakened.
Common Misunderstandings
«This is just language – not healthcare»
Patient information can be “practical,” but it often has a direct impact on the patient’s choices and actions. This is especially true for information about preparations, aftercare, self-care, expected progress, and when the patient should get in touch. When AI contributes to this communication, it becomes relevant to ensure the content is precise, consistent, and professionally grounded.
«If the text is correct once, it’s safe to reuse»
AI-based texts often change with context: small variations in prompts, content, language, or data can yield different answers. What “worked last time” is not necessarily stable the next time. Therefore, control and approval should be tied to the area of use and process – not to individual texts that happened to work well.
«Traceability means only storing the message sent»
Keeping a copy is a minimum, but it does not always meet the need for verifiability. With AI use, it can be equally important to know how the text came about: was it a standard draft, an AI draft, or a combination? Was clinical information added or removed? What changes were made before sending? Without this, learning and discrepancy handling become difficult.
«Responsibility follows the supplier»
Suppliers can be responsible for system features, but the clinic is responsible for how patient communication is conducted in its own practice: which areas of use are allowed, what control points apply, and who can send what. This is particularly relevant when AI is “built in” and experienced as a regular writing assistant.
«An internal policy is sufficient»
General guidelines (“use AI with caution”) rarely provide sufficient control in patient-facing communication. What provides actual governance are concrete clarifications: what types of messages AI can be used for, which always require manual crafting, what must be checked before sending, and how discrepancies are handled.
What should be in place in practice?
For patient information, the goal is usually not to “stop” AI, but to ensure that its use is understandable, controlled, and verifiable. The following governance principles are typically essential:
Mapped actual AI use in patient communication
A practical overview of where AI is involved (email, SMS, portal, chat, internal text suggestions), what it is used for, and what types of information are affected.Clear roles, responsibilities, and decision-making authority
Who can adopt new AI functions? Who approves areas of use and templates? Who owns follow-up on errors, complaints, and discrepancies in patient communication?Human control with clear stops
Define which messages should always be subject to professional quality assurance before sending, and what is the minimum control (fact-checking, tone, risk information, recipient, attachments/links, and ensuring the advice fits the patient's situation).Documentation and traceability in the process
Ensure it is possible to see if AI was used, which version/workflow applied, who edited and approved, and what was sent. This supports both internal learning and verifiability in inspections or complaint cases.Quality standards for patient texts
Establish a set of standards for language and content: what should always be included (e.g., when the patient should contact), what should not be given as general advice, and how uncertainty is phrased plainly.Handling of discrepancies and learning over time
If misphrasing, misunderstandings, or recurring patient questions arise, the clinic should have a simple mechanism to adjust templates, usage rules, and control points – so that improvement is not dependent on individuals.Explanability to patients
The clinic should be able to explain, at a simple level, how communication is structured, what control mechanisms exist, and that the patient can always get clarifications from qualified personnel if needed. This is about trust and predictability, not technical details.
Actera's role in this
Actera is established to provide dental businesses with structure around responsible AI use.
We do not work with technology development or clinical decisions but with governance structure, responsibility lines, and documentation – so that AI can be used in a safe, predictable, and verifiable manner.
Final Consideration
AI will increasingly be part of patient communication in dental clinics, often through features that feel like “regular” writing and work tools. The question is therefore not whether AI affects patient information, but how the clinic ensures control when it happens.
When roles, control points, and traceability are clearly defined, it becomes easier to keep communication precise, handle discrepancies, and explain practices afterward. This provides better predictability for both the clinic, management, and patient.
