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Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

Thirunavukarasu, Arun James; Elangovan, Kabilan; Gutierrez, Laura; Li, Yong; Tan, Iris; Keane, Pearse A; Korot, Edward; (2023) Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial. Journal of Medical Internet Research , 25 (1) , Article e49949. 10.2196/49949. Green open access

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Abstract

Deep learning–based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling “hands-on” education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.

Type: Article
Title: Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial
Location: Canada
Open access status: An open access version is available from UCL Discovery
DOI: 10.2196/49949
Publisher version: https://preprints.jmir.org/preprint/49949
Language: English
Additional information: Copyright © Arun James Thirunavukarasu, Kabilan Elangovan, Laura Gutierrez, Yong Li, Iris Tan, Pearse A Keane, Edward Korot, Daniel Shu Wei Ting. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.10.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
Keywords: Machine learning (1044); automated machine learning (2); autoML; artificial intelligence (719); democratization (1); autonomous AI; imaging (31); image analysis (5); automation (37); AI engineering
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10181005
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