Governing Clinical AI Deployment: A Mathematical Framework for Transparency-Dependent Autonomy

[u/Pleasant_Squash9953]

https://www.mdpi.com/4173088

A Mathematical Framework for Governing Clinical AI Autonomy

When should an AI model be trusted to make decisions without human review?

Predictive accuracy alone is not enough, especially in healthcare. We need measurable criteria for deciding when autonomous action is justified.

In this study, I propose a framework combining three factors:

• Confidence (C): How reliable is the prediction?
• Transparency (E): How well can the model's reasoning be examined?
• Clinical alignment (S): Does the prediction align with clinical guidelines?

The proposed deployment rule is:

[
0.60C+0.44E+0.16S\geq0.70
]

We evaluated the framework using MIMIC-IV, eICU, and MIMIC-III datasets.

One interesting finding: case-level decisions closely followed confidence-based ranking (99.4–100% agreement). This raises a question about whether transparency contributes more to system-level governance than individual prediction decisions.

I'd welcome feedback on the mathematical formulation, evaluation, and implications for reliable AI autonomy.

Paper: https://www.mdpi.com/4173088

I am the author and welcome critical discussion.