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Clinical Evaluation of Artificial Intelligence-Enabled Interventions.

H D Jeffry Hogg^1,2,3, Alexander P L Martindale⁴, Xiaoxuan Liu^1,2,3,5

¹University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.

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|August 6, 2024

Summary

Evaluating artificial intelligence (AI) health technologies requires robust evidence. This guide outlines best practices for generating and appraising AI evidence in healthcare settings to ensure safety and effectiveness.

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Area of Science:

Health Informatics
Medical Artificial Intelligence
Evidence-Based Medicine

Background:

Artificial intelligence (AI) health technologies are rapidly entering clinical practice.
Healthcare systems need methods to assess the safety and effectiveness of AI interventions.
Evaluating AI requires accessible, high-quality evidence tailored to specific healthcare contexts.

Purpose of the Study:

To provide a framework for generating high-quality evidence on AI health technologies.
To empower healthcare decision-makers to evaluate AI interventions within their settings.
To summarize best practices across the AI evidence lifecycle.

Main Methods:

The article reviews best practices for evidence generation in AI health technologies.
Focus areas include study design, conduct, reporting, and appraisal.
The approach synthesizes current knowledge on evaluating AI in healthcare.

Main Results:

Key recommendations are provided for each stage of evidence generation.
Emphasis is placed on ensuring relevance and applicability to real-world healthcare.
The article highlights the importance of context-specific evaluation.

Conclusions:

Adopting standardized good practices is crucial for reliable AI health technology evaluation.
Empowering decision-makers with evidence appraisal skills is essential for safe AI implementation.
This work supports the responsible integration of AI into healthcare systems.