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AI in imaging: the regulatory landscape.

Derek L G Hill¹

¹UCL, Gower Street, London, WC1E 6BT, United Kingdom.

The British Journal of Radiology

|February 17, 2024

Summary

Artificial intelligence (AI) in medical imaging is rapidly advancing, but many studies lack rigor. New regulations demand more robust development and validation for AI medical devices to ensure patient safety and trust.

Area of Science:

Medical Imaging
Artificial Intelligence
Regulatory Science

Background:

Artificial intelligence (AI) applications in medical imaging have grown substantially in recent years.
Despite advancements, a significant portion of AI medical imaging literature exhibits methodological weaknesses.
Existing AI tools often lack the rigorous validation required for clinical integration.

Purpose of the Study:

To highlight the increasing number of AI-enabled medical devices and publications.
To address the identified weaknesses in the current AI medical imaging literature.
To discuss the impact of evolving regulatory requirements on AI medical device development and validation.

Main Methods:

Systematic reviews of AI in medical imaging literature.

Keywords:

AI bias machine learning medical device radiological regulation

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Analysis of evolving regulatory guidance for AI-enabled medical devices.

Discussion of risk mitigation strategies, including bias identification and clinical validation.

Main Results:

A significant increase in AI medical imaging publications and devices.
Identification of substantial weaknesses in a proportion of the existing literature.
Emergence of proactive regulatory frameworks demanding higher standards for AI device development.

Conclusions:

Stricter regulatory requirements, while potentially lengthening development times, are crucial for ensuring the trustworthiness and clinical meaningfulness of AI medical devices.
Addressing risks like bias and ensuring validation in realistic clinical settings are paramount.
Aligning academic research with regulatory frameworks will improve literature quality and facilitate the translation of AI tools into clinical practice.