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Validation and Evaluation as Essentials to Ensuring Safe AI Health Applications.

Michael Rigby¹, Elisavet Andrikopoulou², Mirela Prgomet³

¹Keele University, School of Social, Political and Global Studies and School of Primary, Community and Social Care, Keele, United Kingdom.

Studies in Health Technology and Informatics

|October 3, 2025

Summary

This summary is machine-generated.

Artificial Intelligence (AI) in health informatics requires rigorous scientific validation and safety testing. Establishing robust evaluation principles is crucial to mitigate risks like bias and ensure reliable health AI deployment.

Keywords:

AI effectiveness ethics evaluation evidence safety

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Published on: September 26, 2025

Area of Science:

Health Informatics
Artificial Intelligence
Medical Technology Evaluation

Background:

Artificial Intelligence (AI) is rapidly advancing in health informatics.
Current AI validation lacks the rigor of other health technologies.
Health AI can introduce and perpetuate biases, posing safety and efficiency risks.

Purpose of the Study:

To revisit health informatics principles for AI evaluation.
To establish scientific principles for AI evidence production.
To address the need for rigorous validation of health AI.

Main Methods:

Review of existing health informatics principles and techniques.
Application of the Precautionary Principle for AI evaluation.
Integration of continuous quality improvement methods.

Main Results:

Need for robust evidence and evaluation trails from AI developers.
Call for policy makers to mandate rigorous AI evaluation.
Emphasis on balancing agile evaluation with thoroughness.

Conclusions:

Scientific principles are needed for health AI evaluation.
Agile and pragmatic methods are essential for evolving AI.
Continuous evaluation is key to prevent perpetuation of errors and bias.