Cargando…
Sharing Data With Shared Benefits: Artificial Intelligence Perspective
Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498316/ https://www.ncbi.nlm.nih.gov/pubmed/37642995 http://dx.doi.org/10.2196/47540 |
_version_ | 1785105495665672192 |
---|---|
author | Tajabadi, Mohammad Grabenhenrich, Linus Ribeiro, Adèle Leyer, Michael Heider, Dominik |
author_facet | Tajabadi, Mohammad Grabenhenrich, Linus Ribeiro, Adèle Leyer, Michael Heider, Dominik |
author_sort | Tajabadi, Mohammad |
collection | PubMed |
description | Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner. |
format | Online Article Text |
id | pubmed-10498316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104983162023-09-14 Sharing Data With Shared Benefits: Artificial Intelligence Perspective Tajabadi, Mohammad Grabenhenrich, Linus Ribeiro, Adèle Leyer, Michael Heider, Dominik J Med Internet Res Viewpoint Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner. JMIR Publications 2023-08-29 /pmc/articles/PMC10498316/ /pubmed/37642995 http://dx.doi.org/10.2196/47540 Text en ©Mohammad Tajabadi, Linus Grabenhenrich, Adèle Ribeiro, Michael Leyer, Dominik Heider. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.08.2023. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Viewpoint Tajabadi, Mohammad Grabenhenrich, Linus Ribeiro, Adèle Leyer, Michael Heider, Dominik Sharing Data With Shared Benefits: Artificial Intelligence Perspective |
title | Sharing Data With Shared Benefits: Artificial Intelligence Perspective |
title_full | Sharing Data With Shared Benefits: Artificial Intelligence Perspective |
title_fullStr | Sharing Data With Shared Benefits: Artificial Intelligence Perspective |
title_full_unstemmed | Sharing Data With Shared Benefits: Artificial Intelligence Perspective |
title_short | Sharing Data With Shared Benefits: Artificial Intelligence Perspective |
title_sort | sharing data with shared benefits: artificial intelligence perspective |
topic | Viewpoint |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498316/ https://www.ncbi.nlm.nih.gov/pubmed/37642995 http://dx.doi.org/10.2196/47540 |
work_keys_str_mv | AT tajabadimohammad sharingdatawithsharedbenefitsartificialintelligenceperspective AT grabenhenrichlinus sharingdatawithsharedbenefitsartificialintelligenceperspective AT ribeiroadele sharingdatawithsharedbenefitsartificialintelligenceperspective AT leyermichael sharingdatawithsharedbenefitsartificialintelligenceperspective AT heiderdominik sharingdatawithsharedbenefitsartificialintelligenceperspective |