Responsible AI in Dental Clinics: Which Roles Need to be Clear?

AI functions are increasingly accessible in dental clinics – often as support in text, workflows, planning, and patient communication, and sometimes in more clinical assessment processes. In many cases, AI is “embedded” in tools that the clinic already uses, and therefore does not appear as a separate implementation.

It is precisely this seamless integration that makes role clarification critical. When AI influences how work is done, it becomes necessary to know who owns the usage, who can approve usages, and who should follow up on discrepancies and changes.

This article describes which roles typically need to be clear to use AI responsibly in a dental clinic – and how role ambiguity often arises, even in otherwise well-run organizations.

What is meant by responsible AI and roles in a dental clinic?

“Responsible AI” in dental health is not about being for or against technology. It is about being able to explain and verify how AI is used, in a way that maintains professional quality, patient safety, personal data, and the governance of the organization.

In practice, this means that the clinic has control over:

• what AI is used for (use cases and limitations)

• who has decision-making authority (who can implement and change usage)

• how human oversight is actually exercised (not just assumed)

• how errors and discrepancies are detected and handled

• what is documented, so practice can be verified later

“Roles” in this context are not primarily job titles, but clear functions: who does what and on what mandate. A small clinic may consolidate several roles among few individuals, but the roles must still be explicit – otherwise, accountability is person-dependent and situation-driven.

Where does responsibility arise?

Responsibility questions often arise not at the first use of an AI tool, but when AI becomes part of “how we do it here.” Then it becomes unclear what has been decided, what is habitual, and what is the vendor's standard settings.

Typical places where role ambiguity arises:

• When AI is used without a decision point
  A function is activated in a system, or someone starts using AI to draft texts. It can be efficient, but without a clear “who approves this” mechanism, usage becomes informal.

• When professional responsibility and system responsibility mix
  The person professionally responsible for content (such as patient information or record text) is not necessarily the one who owns the system or can change settings. If this is not clarified, control and follow-up become fragmented.

• When “human control” becomes an assumption
  The clinic may assume that “the dentist always reviews.” In practice, time pressure and trust in the system's suggestions may make the control less active. Without clear control points and responsibility for quality assurance, it becomes difficult to verify.

• When discrepancies do not have a clear recipient
  If employees discover errors, biases, or unfortunate formulations: Who should be notified? Who assesses the seriousness? Who can stop usage or change routines? Without this, discrepancies are often “solved in the moment,” without learning.

• When vendor management becomes too weak
  AI changes through updates, new functions, and changed user interfaces. If no one is responsible for change management, the clinic may effectively get new functionality without assessing the consequences.

Common misunderstandings

“This is an IT responsibility”

IT (internal or external) can own operation and access, but AI usage affects professional practice, patient contact, documentation, and workflows. This means responsibility must be rooted in management and operations, with IT as a support function where relevant.

“If the vendor has created it, the vendor has the responsibility”

The vendor may be responsible for product and functionality, but the clinic is responsible for how the tool is used in practice: what uses are allowed, how employees work with drafts and suggestions, what control points apply, and how discrepancies are handled.

“We are too small to have roles – we just do it”

Small clinics often need clearer role descriptions, not fewer. When few individuals cover many tasks, the risk increases for critical tasks “falling through the cracks” or being solved differently each time.

“A policy on cautious use is enough”

General guidelines rarely provide guidance in a hectic everyday life. Responsible AI requires operationalization: decision points, minimum control requirements, documentation requirements, and a practical discrepancy process.

“Roles are about finding someone to blame”

Role clarification is primarily about predictability: who has the mandate to decide, the duty to follow up, and the responsibility to document. It makes discrepancy handling less person-oriented and more system-oriented.

What should be in place in practice?

Below are a set of roles/functions that often should be clear in a dental clinic using AI. They can be combined, but should be described explicitly.

• 1) AI Owner (organizational responsibility/clinic management)
  Has overall responsibility for ensuring AI usage aligns with the clinic's practice, risk level, and internal requirements. Decides which uses are allowed and prioritizes resources for control and follow-up.

• 2) Professional responsibility for content and practice
  Owns professional quality requirements where AI affects assessments, record texts, patient information, or workflows that can have professional consequences. Defines what must always be manually assessed and what constitutes minimum control.

• 3) System owner/application responsible
  Responsible for system setup, access control, configuration, and understanding which AI functions are actually active. Ensures changes and updates are detected and assessed, not just “rolled out.”

• 4) Responsible for information security and privacy (function, not title)
  Ensures AI usage does not create unwanted data sharing, wrong recipient issues, lack of access control, or weak logging. Assesses where data is processed, how it is stored, and if there are internal limitations on what AI can be used on.

• 5) Data and quality responsible (for data basis and documentation quality)
  With AI using or affecting data (such as templates, record texts, structured fields), someone should be responsible for data quality, terminology, and “hygiene”: what is reliable, what is incomplete, and how errors are corrected in a traceable way.

• 6) Training responsible/superuser function
  Responsible for ensuring employees understand how AI is used locally: limitations, typical errors, what should be double-checked, and when to stop or escalate. This is often crucial for making “human control” real.

• 7) Discrepancy and change responsible (can be the same as AI Owner)
  Defines what is considered a discrepancy in AI usage, how it is reported, how severity is assessed, and who can initiate measures (pause function, change routine, contact vendor, update templates).

In addition, the clinic should establish a simple management approach that makes the roles operational:

• A usage registry: which AI use cases are approved, restrictions, responsible roles, and control points.

• RACI for the most important processes: who is Responsible, Accountable, Consulted, Informed for implementation, change, daily use, and discrepancies.

• Minimum documentation requirements: what must be explained later (use case, responsibility, control, event flow for discrepancies).

This does not need to be extensive, but it must be consistent and executable.

Actera's role in this

Actera is established to provide dental health organizations with structure around the responsible use of AI.

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

Final consideration

When AI becomes part of everyday operations, it is role ambiguity – not the technology itself – that often creates risk. Without clear functions for decision-making, control, and follow-up, AI usage becomes informal, person-dependent, and difficult to verify.

Clear roles make it easier to use AI in a way that can be explained later: what is allowed, who has assessed the usage, how quality is ensured, and how the clinic learns when something does not work as expected.