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Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial.

Arun James Thirunavukarasu^1,2, Kabilan Elangovan², Laura Gutierrez²

¹University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.

Journal of Medical Internet Research

|October 12, 2023

Summary

This summary is machine-generated.

Automated machine learning (autoML) platforms democratize artificial intelligence (AI) in medicine by simplifying deep learning for clinicians. This technical overview covers autoML applications in education, research, and clinical practice, emphasizing ethical and best practices.

Keywords:

AI engineering artificial intelligence autoML automated machine learning automation autonomous AI democratization image analysis imaging machine learning

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Area of Science:

Medical Artificial Intelligence
Machine Learning Engineering
Clinical Informatics

Background:

Deep learning in clinical imaging analysis powers diagnostic AI, matching or surpassing expert performance and revolutionizing healthcare.
Automated machine learning (autoML) platforms reduce technical barriers, enabling clinicians with limited expertise to leverage AI, including foundation models like large language models.

Purpose of the Study:

To provide a technical overview of autoML platforms and their applications in medical education, research, and clinical practice.
To outline the stages of an autoML project, emphasizing ethical and technical best practices.
To discuss the strengths and limitations of various autoML platforms (code-free, code-minimal, code-intensive).

Main Methods:

Review and technical overview of autoML processes.
Description of autoML application stages: data acquisition, partitioning, model training, validation, analysis, and deployment.
Evaluation of different autoML platform types based on coding requirements.

Main Results:

AutoML democratizes AI in medicine, enhancing AI literacy through hands-on education.
AutoML facilitates rapid research testing and benchmarking, optimizing resource allocation.
AutoML can be applied in clinical settings, pending regulatory compliance, and promotes data-centric development.

Conclusions:

AutoML holds significant potential to advance AI adoption in medicine by simplifying complex processes.
Effective and ethical implementation requires comprehensive education for clinicians on autoML technologies.
AutoML supports a shift towards data-centric approaches in medical AI development.