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AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond

Here, we introduce Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI), a multidisciplinary data-generation project designed to create and share a multimodal dataset optimized for artificial intelligence research in type 2 diabetes mellitus.

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Fig. 1: Data collection protocol for each participant in AI-READI.

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Acknowledgements

This work was supported by the US National Institutes of Health (NIH) through grants OT2OD032644 and P30 DK035816. We thank the Microsoft AI for Good Lab for supporting the cloud services needed for the project. We thank Topcon Corporation (Tokyo, Japan), Optomed (Oulu, Finland), iCare World (Raleigh, NC) and Carl Zeiss (Oberkochen, Germany) for loaning their devices for research purposes at no cost. We thank Heidelberg Engineering (Heidelberg, Germany), Dexcom (San Diego, CA) and Garmin (Olathe, KS) for research discounts on study devices. We also thank the study participants and the AI-READI Advisory Council.

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The Writing Committee members created the first draft, which was reviewed, edited and approved by all the authors.

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Correspondence to Aaron Y. Lee.

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S.B.: funding — NIH, University of California Office of the President, Research to Prevent Blindness; consultant — Topcon; equipment — Optomed. V.R.d.S.: funding — NSF, UCSD Social Sciences, Sanford Institute for Empathy and Compassion (Center for Empathy and Technology), Intel, Mathworks, UCSD instructional improvement grant, equipment funding from Adobe and NVIDIA, Kavli Institute for Brain and Mind, IBM, past funding from Sony; member — Cognitive Science Society Governing Board. K.F.: member — Institutional Review Board for the All of Us Research Program, Digital Ethics Advisory Panel for Merck KGaA (Merck Germany). C.S.L.: funding — NIH, Alzheimer’s Disease Drug Discovery Foundation, Gates Ventures, Research to Prevent Blindness. T.Y.A.L.: funding — Research to Prevent Blindness, Dr. H. James and Carole Free Career Development Award. B.P.: funding — NIH. L.M.Z: funding — NEI, NIH, The Glaucoma Foundation, Heidelberg Engineering, DRCR Retina Network/JAEB Center for Health Research, The Krupp Foundation; receipt of equipment, materials, software — Optomed, ICare, Topcon, Heidelberg Engineering, Carl Zeiss Meditec, Optovue/Visionix; consultant — Abbvie, Topcon Medical Systems; co-founder, inventor, board member, equity holder — AISight Health Inc. S.H.: funding — NIH, NIH/NARCH, RWJF, UCSD Herbert Wertheim School of Public Health. H.I.: funding — NIH; founder, stock holder — Gobiquity, Inc. A.Y.L.: funding — Santen, Topcon, Carl Zeiss Meditec, Regeneron, Amazon, Meta, Research to Prevent Blindness; personal fees — Genentech, Sanofi, US FDA, Johnson and Johnson, Boehringer Ingelheim, Gyroscope; non-financial support — iCareWorld, Optomed, Heidelberg, Microsoft. S.M.: Funding — NIH, Edward P. Evans Foundation, OHSU Knight Cancer Institute; receipt of in-kind contribution — Nike. C.N.: funding — NIH, NSF, PCORI. C.O.: consultant — Johnson and Johnson. L.H.: funding — NIH Grant UL1TR001442; consultant — Bristol Myers Squibb. The remaining authors declare no competing interests.

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Supplementary Figs. 1–3 and Table 1

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AI-READI Consortium. AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond. Nat Metab 6, 2210–2212 (2024). https://doi.org/10.1038/s42255-024-01165-x

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