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

NIH Program Scientists; Interns; Project Managers; Research, Technical and Clinical Staff; Principal Investigators; Writing Committee; AI-READI Consortium

doi:10.1038/s42255-024-01165-x

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

NIH Program Scientists, Interns, Project Managers, Research, Technical and Clinical Staff, Principal Investigators, Writing Committee, AI-READI Consortium

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

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.

Original language	English (US)
Journal	Nature Metabolism
DOIs	https://doi.org/10.1038/s42255-024-01165-x
State	Accepted/In press - 2024

ASJC Scopus subject areas

Internal Medicine
Endocrinology, Diabetes and Metabolism
Physiology (medical)
Cell Biology

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10.1038/s42255-024-01165-x

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@article{d71efa7787dd45d1aa0c2829bb3756ab,

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

abstract = "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.",

author = "{NIH Program Scientists} and Interns and {Project Managers} and {Research, Technical and Clinical Staff} and {Principal Investigators} and {Writing Committee} and {AI-READI Consortium} and Xujing Wang and Kyongmi Simpkins and Muna Mohammed and Hadi Khazaei and Apoorva Karsolia and Kalaw, {Fritz Gerald P.} and Lingling Huang and Anna Heinke and Shahin Hallaj and Nada Haboudal and Monique Bangudi and Amankwa, {Charles E.} and Brittany York and Kate Watkins and Hanna Pittock and Dawn Matthies and Abigail Lucero and Caroline Drolet and Kevin Zhao and Sanjay Soundarajan and Jamie Shaffer and Victoria Patronilo and Caitlyn Ngadisastra and Jessica Mitchell and Lin, {Wei Chun} and Trina Kim and Stephanie Hong and Nayoon Gim and Aydan Gasimova and Colleen Cuddy and Katie Crist and Benjamin Cordier and Aaron Cohen and Marian Blazes and Erik Benton and Adrienne Baer and Catherine Ashley and Arash Alavi and Mohammad Adibuzzaman and Riddhiman Adib and Singer, {Sara J.} and Cynthia Owsley and Camille Nebeker and Shannon McWeeney and Gerald McGwin and Lee, {Aaron Y.} and Hiroshi Ishikawa and Samantha Hurst and Edberg, {Jeffrey C.} and Chute, {Christopher G.}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Limited 2024.",

year = "2024",

doi = "10.1038/s42255-024-01165-x",

language = "English (US)",

journal = "Nature Metabolism",

issn = "2522-5812",

publisher = "Springer Berlin",

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TY - JOUR

T1 - AI-READI

T2 - rethinking AI data collection, preparation and sharing in diabetes research and beyond

AU - NIH Program Scientists

AU - Interns

AU - Project Managers

AU - Research, Technical and Clinical Staff

AU - Principal Investigators

AU - Writing Committee

AU - AI-READI Consortium

AU - Wang, Xujing

AU - Simpkins, Kyongmi

AU - Mohammed, Muna

AU - Khazaei, Hadi

AU - Karsolia, Apoorva

AU - Kalaw, Fritz Gerald P.

AU - Huang, Lingling

AU - Heinke, Anna

AU - Hallaj, Shahin

AU - Haboudal, Nada

AU - Bangudi, Monique

AU - Amankwa, Charles E.

AU - York, Brittany

AU - Watkins, Kate

AU - Pittock, Hanna

AU - Matthies, Dawn

AU - Lucero, Abigail

AU - Drolet, Caroline

AU - Zhao, Kevin

AU - Soundarajan, Sanjay

AU - Shaffer, Jamie

AU - Patronilo, Victoria

AU - Ngadisastra, Caitlyn

AU - Mitchell, Jessica

AU - Lin, Wei Chun

AU - Kim, Trina

AU - Hong, Stephanie

AU - Gim, Nayoon

AU - Gasimova, Aydan

AU - Cuddy, Colleen

AU - Crist, Katie

AU - Cordier, Benjamin

AU - Cohen, Aaron

AU - Blazes, Marian

AU - Benton, Erik

AU - Baer, Adrienne

AU - Ashley, Catherine

AU - Alavi, Arash

AU - Adibuzzaman, Mohammad

AU - Adib, Riddhiman

AU - Singer, Sara J.

AU - Owsley, Cynthia

AU - Nebeker, Camille

AU - McWeeney, Shannon

AU - McGwin, Gerald

AU - Lee, Aaron Y.

AU - Ishikawa, Hiroshi

AU - Hurst, Samantha

AU - Edberg, Jeffrey C.

AU - Chute, Christopher G.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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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U2 - 10.1038/s42255-024-01165-x

DO - 10.1038/s42255-024-01165-x

M3 - Comment/debate

AN - SCOPUS:85208785275

SN - 2522-5812

JO - Nature Metabolism

JF - Nature Metabolism

ER -

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

Abstract

ASJC Scopus subject areas

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