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. 2022 May 2;14:28. doi: 10.1186/s11689-022-09438-w

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — Applications of ML/AI-driven research. Some of the key studies we discussed in Machine learning methods and applications to IDDs section of this review are summarized. It is clear that the application of AI/ML (colored edges with references) to multimodal data types (depicted in the middle layer) has the potential to be useful in enhancing clinical decision-making (top layer) as well as developing a mechanistic understanding of IDDs (bottom layer)