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Special Section Guest Editorial: Evaluation Methodologies for Clinical AI.

Susan M Astley¹, Weijie Chen², Kyle J Myers²

¹University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, United Kingdom.

Journal of Medical Imaging (Bellingham, Wash.)

|March 25, 2020

Summary

This summary is machine-generated.

This editorial introduces a special section focused on evaluation methodologies for clinical artificial intelligence (AI). It highlights the need for robust methods to assess AI tools in healthcare settings.

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Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Healthcare Technology Evaluation

Background:

Clinical artificial intelligence (AI) is rapidly advancing, necessitating rigorous evaluation frameworks.
Current methods for assessing clinical AI may not adequately address unique challenges in healthcare.
Standardized evaluation is crucial for safe and effective AI implementation.

Purpose of the Study:

To introduce a special section dedicated to novel evaluation methodologies for clinical AI.
To emphasize the importance of developing and applying robust AI assessment techniques in medicine.
To foster discussion on best practices for clinical AI validation.

Main Methods:

This editorial outlines the scope and objectives of the special section.
It highlights key themes and contributions within the section.
It frames the ongoing discourse on AI evaluation in healthcare.

Main Results:

The special section will feature diverse perspectives on clinical AI evaluation.
It aims to consolidate current knowledge and identify future research directions.
The collection underscores the critical need for specialized evaluation approaches.

Conclusions:

Effective evaluation methodologies are paramount for the responsible integration of AI in clinical practice.
Continued research and development in AI assessment are essential for patient safety and efficacy.
This special section serves as a catalyst for advancing the field of clinical AI evaluation.