A suggestion is not a decision
AI can help clinicians investigate complex genetic cases. It can organise evidence, prioritise hypotheses, find relevant literature and suggest how findings might be interpreted.
Proposing an interpretation is different from taking responsibility for it.
A genetic classification can influence further testing, clinical management and decisions that affect patients and their families. That responsibility cannot be handed to a model.
AI can support clinical judgment. It cannot carry clinical accountability.
Why human review matters
AI systems can produce convincing explanations that are incomplete, misleading or simply wrong.
A proposed classification may miss an important clinical observation. The supporting evidence may be weaker than it looks. A finding may conflict with information that was not available during the analysis.
These limits matter in clinical genetics, where uncertainty is common and evidence can be hard to interpret.
In Tulparus, AI-generated assessments stay open to professional scrutiny before they become approved clinical interpretations. The clinician can examine the evidence, question the reasoning and reach an independent judgment.
Human review does not remove mistakes. It is the chance to catch them before a decision is final.
Accountability belongs to people
When a clinical interpretation is approved, it should be clear who reviewed it, what evidence informed the decision and when it was made.
This is more than an administrative requirement. Interpretations are revisited months or years later, sometimes after new discoveries have changed the understanding of a condition. A documented decision lets later reviewers see the earlier conclusion in its original context.
It also records something an automated system cannot take on: professional responsibility.
Automation should create time for judgment
Clinical genetics involves a great deal of work beyond the conclusion itself. The literature has to be reviewed, evidence assessed and competing explanations weighed.
Much of that work can be supported by automation. The point is to give clinicians more time for the judgment only they can make, never to take that judgment away.
A model proposes. A person reviews, decides and signs off.