AI in Dental Clinics: Who is Responsible?

Artificial intelligence is already being used in dental clinics—from administrative processes to decision support, text suggestions, and workflow automation. Its use may be visible or “embedded” in systems that feel like regular digital tools.

When AI influences priorities, assessments, or communication, a lingering question arises: Who is actually responsible—the practitioner, clinic management, or supplier?

This article explains how responsibility related to AI use should be understood in dental clinics, where responsibility typically becomes unclear, and which governance principles should be in place to ensure predictability and traceability.

What is meant by AI use in Dental Health?

In a dental clinic, “AI use” rarely refers to one clear, defined solution. The term encompasses various support functions and automation that can affect how the clinic operates.

Typical examples include:

• Clinic-related support: suggestions for wording, text summarization, triaging or prioritizing tasks, data quality checks or deviation signals in workflow.

• Administrative uses: automated scheduling, resource allocation, insight reports, or predictions related to absences and capacity.

• Communication: drafts for patient information, standard texts, reply suggestions, or translations.

Common to these areas is that AI can affect what is suggested, what is highlighted, and how information is formulated. This does not mean that AI “makes decisions” in a legal or clinical sense, but it can influence the decision-making basis and thereby the responsibility framework.

Where Does the Responsibility Question Arise?

The responsibility question typically arises in transitions between human judgment and technological support—especially when it is unclear what is automated and who has the mandate to approve, override, or stop the use.

Some recurring situations are:

• Unclear boundaries in workflow
  When staff follow recommendations from a system “that always appears to be correct,” a gradual effect can occur where judgments become less active without anyone deciding it should be that way.

• Use of third-party solutions
  Many AI functions are delivered by external actors or integrated into software the clinic already uses. The clinic may feel like it is “just a user,” while responsibility must still be rooted in its own governance and practice.

• Lack of role clarification
  Who can implement new AI functions? Who assesses risk? Who follows up on deviations? If this is not defined, responsibility easily becomes person-dependent and situationally driven.

• Weak documentation and traceability
  When it is difficult to reconstruct what happened later (what data were used, what recommendations were given, and what evaluations were made), responsibility clarification becomes similarly difficult.

Increasing regulatory expectations for responsible AI are also part of the backdrop. The EU has adopted the AI Act, which came into effect on August 1, 2024, with a gradual introduction of requirements over time. For dental health, it is particularly relevant that some AI systems may be classified as “high risk” depending on their use and whether they are part of regulated product categories (such as medical equipment). (This is described as principles and context, not legal advice.)

Common Misunderstandings

“AI Makes the Decision”

In practice, AI is typically a support function: it suggests, ranks, summarizes, or classifies. It might still feel decision-like when the suggestions are presented with high authority or become standard choices in the system.

Responsibility therefore often arises not in the “moment AI answers,” but in how the clinic has defined the usage: Which tasks can be supported? When must a professional make an independent judgment? How is override ensured when something seems wrong?

“Responsibility Follows the Supplier”

The supplier may have responsibility related to product, quality, and documentation, but the clinic cannot “outsource” responsibility for how a system is used in its own operations. The clinic sets the context, process, training, control, and how recommendations actually impact work.

In regulatory logic, this is often referred to as responsibility of the party who implements and operates the solution (deployment/user role), including human oversight and ongoing follow-up. 

“This is Solved with Internal Policy”

Guidelines are useful, but alone are often too general: “Use AI with caution” provides little direction when doubt arises in practice.

Responsibility becomes clear only when policy is linked with specific mechanisms: roles, approval of application areas, training, logging/traceability, and practical checkpoints in the workflow.

“If It's Just Text Suggestions, the Risk is Low”

Text suggestions may seem harmless but can influence precision in records, the tone in patient communication, and what information is actually documented. Small changes in wording can make a significant difference in what can be later reviewed.

Therefore, responsibility is not just about “clinical decision,” but also about the quality of documentation, information flow, and how the clinic ensures human judgment actually occurs.

“Responsibility is About Finding Someone Guilty”

In governance terms, responsibility primarily means ensuring someone has the mandate and obligation to: set boundaries, follow up practices, and manage deviations. When this is unclear, problems often fall to individuals afterward.

A more robust responsibility framework describes in advance who does what and what should be documented—so that traceability does not become an ad hoc project when something goes wrong.

What Should Be in Place Practically?

When a dental clinic wants to use AI predictably, there are often five areas that distinguish “random use” from structured practice:

• Mapping of actual AI use
  An overview of where AI is part of systems and workflows (even where it is not obvious), what it is used for, and what data are affected.

• Clear roles, responsibilities, and decision authority
  Who can introduce new application areas? Who approves changes? Who owns risk and deviation management? This should be defined at the clinic level, not just as general “IT follow-up.”

• Human control and ability to override
  Clear expectations for when staff should stop, override, or double-check. This requires both competence and practical space in the workflow to exercise control. 

• Documentation and traceability
  It should be possible to explain afterward: which application area was in use, what was suggested/automatically processed, and how a professional assessed the outcome. Without this, responsibility is difficult to verify.

• Explanability to patients and oversight
  The clinic should be able to describe at an understandable level what AI is used for, what it is not used for, and what control mechanisms exist. The goal is not technical detail, but trust through verifiable practice.

Actera's Role in This

Actera is established to provide dental health practices with a structure around responsible AI use.

We do not work with technology development or clinical decisions, but with governance framework, responsibility lines, and documentation—so that AI can be used in a safe, predictable, and verifiable manner.

Final Consideration

AI will continue to be part of the dental clinic. The question is rarely whether the technology exists, but how it actually influences workflow, assessments, and documentation.

Clear responsibility thus focuses less on individual tools, and more on structure: roles, control points, and traceability that withstand scrutiny. When this is in place, it becomes simpler to use AI in a way that provides predictability for clinics, management, and patients.