Human Control in AI Use: What Does It Mean in Clinical Work?
AI functions are quickly becoming part of clinical work in dental care—often as support in text, workflow, prioritization, and communication, and sometimes as decision-near support.
When such features are perceived as "built-in" and frictionless, it is easy to assume that human control is already taken care of—because a professional is involved anyway.
This article explains what human control in AI use actually entails in practice, where responsibility and risk typically arise, and what governance principles should be in place to make control verifiable.
What is meant by AI use in dental care?
In dental care, AI use often means that systems suggest, summarize, structure, or rank information—without necessarily appearing as an "AI tool." Examples can include:
Text-related support: drafts of medical records, summaries, standard formulations, or suggestions for patient information.
Workflow and prioritization: sorting of inquiries, suggestions for task prioritization, or automatic "next steps" in the process.
Quality and deviation signals: alerts about deficiencies, deviation patterns, or data inconsistencies.
The crucial factor for "human control" is not whether AI is used, but how AI influences action: What is suggested as standard? What is left out? Where is one tempted to approve quickly? And where is it practically difficult to override?
Where does the responsibility question arise?
The responsibility question typically arises when human control is an intention, but not a defined mechanism. It becomes unclear who should control what, when, and with what mandate.
Some recurring situations in clinical work are:
When AI content "looks right"
AI-generated text or suggestions can be linguistically convincing, but factually incorrect or too general. The risk becomes especially high when time pressure leads to reading replacing real assessment.When the control role is not explicitly assigned
Effective human control presupposes that it is actually designated who has the responsibility to exercise it, and that the person has competence, authority, and support. This is also a clear expectation in the EU AI Act for deployers of high-risk AI.When AI shifts attention, not just time
In practice, control often concerns attention: AI can "steer the gaze" toward certain findings and away from others, or suggest a standard path that is followed without being challenged.When there are no real stopping points or override possibilities
If the workflow does not have clear points where one should stop, double-check, or document deviations from AI suggestions, control becomes practically optional and dependent on individuals.When traceability is lacking in case of deviations or complaints
In incidents, it is often necessary to be able to explain: What was suggested by the system? What did the professional do? What was changed? NIST's AI RMF indicates that processes for human control should be defined, assessed, and documented—precisely to make governance verifiable.
Common Misconceptions
"Human control means that a dentist is involved"
Having a professional in the process is not the same as control actually being exercised. Control must be operationalized: what should be assessed, which signs should trigger a stop, and what to do when disagreeing with the system's suggestion.
"This only applies to clinical AI systems"
Control needs also arise in "administrative" and communicative AI. Text suggestions in records and patient communication can affect precision, documentation quality, and patient's understanding—even if AI does not make a clinical assessment in the narrow sense.
"If the provider has built in control, we are covered"
Providers can support with functions, but the business must define the use case, roles, training, and follow-up in its context. The EU AI Act distinguishes between provider and deployer responsibilities and points out that deployers must ensure competent human control in use.
"Human control is a policy, not a practice"
A sentence in an internal guideline (“a professional assessment shall always be made”) provides little governance if it is not linked to specific control points, documentation, and deviation procedures.
"Control means AI cannot be used effectively"
Human control does not mean removing benefits, but ensuring that efficiency does not come at the expense of verifiability and professional precision. In practice, the goal is to define where control is necessary, and make it easy to perform consistently.
What should be in place in practice?
For dental clinics, a good goal is to establish a control model that is easy to follow in daily life and able to withstand scrutiny in retrospect.
Mapped actual AI use in the business
Overview of where AI affects clinical work (records, patient info, prioritization, follow-up), and what is practically “automatic” versus professional assessment.Clear roles, responsibilities, and decision-making authority
Designate who has control responsibility in the most important use areas, and ensure that the role has the mandate to stop/change practice when necessary. The Health Directorate also points out that operation and management of AI may require clear roles and responsibilities, and that governance systems can be expanded to manage AI quality over time.Human control and possibility for override
Make control concrete:which elements must always be verified (facts, clinical assessments, risk information, recipient),
which situations require double-checking,
what are the stop criteria,
how disagreement or correction is documented.
For deployers of high-risk AI, the expectation of competent human oversight is explicit, tied to competence, training, authority, and support.
Documentation and traceability
At a minimum, the clinic should be able to explain:which usage area was in operation,
who exercised control,
what was assessed/adjusted,
and how deviations are handled.
The EU AI Act also describes logging requirements for deployers of high-risk AI (minimum retention), illustrating the trend towards more verifiable operations.
Explainability to patients and regulatory authorities
The clinic should be able to describe on a practical level what AI is used for, what it is not used for, and what control mechanisms exist. This is about predictability, not technical details.
Actera's Role in This
Actera is established to provide dental health organizations with a structure around responsible AI use.
We do not work with technology development or clinical decisions, but with governance structure, lines of responsibility, and documentation so that AI can be used in a safe, predictable, and verifiable manner.
Concluding Thoughts
Human control in AI use is not a principle that "arises by itself" because a professional is involved. It is a practice that must be clearly defined, assigned, and supported—and must be explainable in hindsight.
When control is operationalized through roles, stopping points, and traceability, AI use becomes easier to manage over time. It reduces person dependency and makes it possible to combine efficiency with verifiable clinical practice.
