Responsibility When AI Is Used Differently by Different Practitioners
Introduction
AI tools are rarely used in exactly the same way by all practitioners in a clinic. Some use them actively as support in assessments and documentation, while others use them more sporadically—or in completely different ways than originally intended.
This variation often occurs without explicit decisions or common clarifications. Nonetheless, it can impact quality, documentation, patient safety, and traceability.
This article explains how different AI use among practitioners creates a responsibility issue at the organizational level, why this is about practice deviations—not just system choices—and what should be in place to manage this as a governance responsibility.
What Is Meant by Different AI Use in the Clinic?
Different AI use refers to variations in how practitioners actually use the same AI tools in their daily work.
This can, for example, mean that:
– some practitioners actively use AI in preparation and assessment, while others do not
– AI is used in different parts of the patient process depending on the practitioner
– output is interpreted and emphasized differently
– AI is used more or less critically, with varying degrees of override
– some use the tool for purposes it was not intended for
Such variation is not uncommon in itself. The question arises when different practices lead to varying decision-making bases, documentation, or service quality.
Where Does the Responsibility Issue Arise?
The responsibility issue arises when the organization allows—explicitly or implicitly—AI to be used in significantly different ways without assessing the consequences.
This is particularly evident at three levels:
Quality and Equality
When AI influences assessments or documentation differently, patients can effectively encounter varying work processes and decision-making bases.Traceability
If AI is involved in work in different ways, it becomes challenging to understand in retrospect how assessments were actually made.Responsibility and Governance
When practice varies, yet no one has defined what is correct or expected use, a governance vacuum occurs.
This is not primarily about individual practitioners' choices, but about the organization's responsibility for common practice.
Common Misunderstandings
“This Is About Personal Work Style”
When AI affects professional assessments and documentation, it is no longer just a matter of style. It becomes part of the clinic's overall practice.
“Experienced Practitioners Should Use AI As They Wish”
Professional judgment is central, but freedom requires common frameworks. Without clarified expectations, variation becomes unstructured.
“As Long as AI Is Voluntary, the Responsibility Is Individual”
Voluntary use does not relieve the organization of responsibility if the use impacts quality, priorities, or documentation.
“This Can Be Managed as Needed”
Once different practices are established, they are harder to change. Lack of early governance turns deviations into the norm.
What Should Be in Place in Practice?
To manage different AI use as a governance issue, the organization should take responsibility for the practice level—not just the system level.
This involves, among other things:
– Clarified Purpose and Expected Use
It should be clear what AI is to be used for, and which parts of the work it should not affect.
– Shared Understanding of Correct Use
Practitioners should have a shared understanding of how AI fits into workflows and assessments.
– Room for Professional Judgment Within Boundaries
Variation can be legitimate, but should occur within clarified limits.
– Attention to Practice Deviations
Different usage patterns should be monitored, not to control individuals, but to understand overall practice.
– Documentation of How AI Is Integrated into Work
When AI is used differently, this should be known and manageable in governance.
This shifts the focus from “who did what” to “what practice do we allow.”
Actera’s Role in This
Actera is established to provide dental health organizations with structure around responsible AI use.
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 traceable manner.
Concluding Consideration
Different AI use among practitioners is not a deviation in itself but becomes a responsibility issue when the variation affects quality, equality, and traceability.
Taking responsibility for practice involves making expectations, boundaries, and actual use visible. When this is managed at the organizational level, AI can support professional judgment without creating undesirable practice differences.
