When AI Gives Different Answers Over Time: Stability as a Risk Factor
Introduction
AI is often evaluated based on accuracy: whether the answers are correct here and now. However, in clinical practice, consistency over time is at least equally important. When the same question, data, or situation yields different AI responses at different times, new forms of risk arise.
Such variations can be difficult to detect in everyday practice. Nevertheless, they affect documentation, diagnostics, traceability, and trust—both internally in the clinic and towards patients and supervisory authorities.
This article explains why stability and consistency in AI output are governance-relevant in the clinic, how model changes and version issues manifest in practice, and what should be in place to manage this as a governance matter.
What Is Meant by Stability and Consistency in AI?
Stability is about whether an AI system provides predictable results over time, given the same or similar input. Consistency refers to the degree of agreement between answers across time, users, or contexts.
In a clinical context, lack of consistency can manifest as:
– different formulations or assessments in journal drafts for the same issue
– varying suggestions or summaries with similar clinical data
– changed priorities or analyses without a clear reason
– differences that cannot be explained scientifically or methodologically
These variations may be small individually but become significant when AI is used repeatedly in work requiring coherence, traceability, and explainability.
Where Does the Risk Arise?
The risk arises at the intersection of clinical practice and technological change. AI systems are not static. They can change over time through:
– updates of underlying models
– adjustments in training data or parameters
– changes in how the system is configured or integrated
– adaptation to new contexts or usage patterns
For the clinic, this can mean that a tool that "worked as before" suddenly provides different answers—without this being clearly communicated or documented.
The consequences appear particularly in three areas:
Record Keeping
Different formulations over time can weaken consistency in documentation and make verification challenging.Diagnostic Support
Varying assessments can affect clinical reasoning, especially where AI is used as support in complex cases.Explainability and Trust
When output is not stable, it's difficult to explain why an assessment was made—both internally and externally.
Common Misunderstandings
“Variation is a Sign of Intelligence”
In clinical work, predictability is often a prerequisite for quality. Unexplained variations can create uncertainty, even if each individual answer seems plausible on its own.
“This is Just a Technical Issue”
For the clinic, this is a practice issue. When output changes, the basis for decision-making also changes—regardless of the technical cause.
“As Long as the Answers Are Correct, It Doesn’t Matter”
Correctness without consistency is difficult to verify. Over time, lack of stability can undermine both quality assurance and learning.
“We Will Notice if It Becomes a Problem”
Changes often occur gradually. Small variations can become normalized in daily practice and only become visible when the consequences have already occurred.
What Should Be in Place in Practice?
To manage stability as a risk factor, the organization should treat model change and consistency as part of its governance responsibility.
This includes, among other things:
– Awareness that AI Changes Over Time
AI should be understood as dynamic systems, not fixed tools.
– Overview of Which AI Tools Are Used Where
This makes it possible to evaluate consequences when output changes.
– Procedures for Change and Versioning
It should be known when significant changes occur, and which parts of practice they affect.
– Assessment of Consistency in Critical Areas of Use
Record keeping, diagnostics, and decision support should receive special attention.
– Documentation of Use Context
When AI is part of assessments, it should be known and verifiable in retrospect.
This way, the clinic can distinguish between desired development and unwanted variation.
Actera's Role in This
Actera is established to provide dental health enterprises with a structure around the responsible use of AI.
We do not work with technology development or clinical decisions, but with governance structure, responsibility lines, and documentation—so AI can be used in a safe, predictable, and verifiable way.
Final Reflections
AI that changes answers over time challenges clinical practice in a different way than obvious errors. The risk lies not only in what is right or wrong but in the loss of consistency and explainability.
Taking stability seriously involves monitoring how AI actually behaves in practice—over time. When change is understood and managed, AI can evolve without undermining documentation, quality, or trust.
