The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI).
The application of machine learning and artificial intelligence to clinical settings for prevention, diagnosis, treatment, and the improvement of clinical care have been demonstrably cost-effective. However, current clinical AI (cAI) support tools are predominantly created by non-domain experts and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-02-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000011 |
| _version_ | 1797704106582736896 |
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| author | Julian Euma Ishii-Rousseau Shion Seino Daniel K Ebner Maryam Vareth Ming Jack Po Leo Anthony Celi |
| author_facet | Julian Euma Ishii-Rousseau Shion Seino Daniel K Ebner Maryam Vareth Ming Jack Po Leo Anthony Celi |
| author_sort | Julian Euma Ishii-Rousseau |
| collection | DOAJ |
| description | The application of machine learning and artificial intelligence to clinical settings for prevention, diagnosis, treatment, and the improvement of clinical care have been demonstrably cost-effective. However, current clinical AI (cAI) support tools are predominantly created by non-domain experts and algorithms available in the market have been criticized for the lack of transparency behind their creation. To combat these challenges, the Massachusetts Institute of Technology Critical Data (MIT-CD) consortium, an affiliation of research labs, organizations, and individuals that contribute to research in and around data that has a critical impact on human health, has iteratively developed the "Ecosystem as a Service (EaaS)" approach, providing a transparent education and accountability platform for clinical and technical experts to collaborate and advance cAI. The EaaS approach provides a range of resources, from open-source databases and specialized human resources to networking and collaborative opportunities. While mass deployment of the ecosystem still faces several hurdles, here we discuss our initial implementation efforts. We hope this will promote further exploration and expansion of the EaaS approach, while also informing or realizing policies that will accelerate multinational, multidisciplinary, and multisectoral collaborations in cAI research and development, and provide localized clinical best practices for equitable healthcare access. |
| first_indexed | 2024-03-12T05:15:26Z |
| format | Article |
| id | doaj.art-2cdea64f1d2d4a559833c65202e9e30b |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T05:15:26Z |
| publishDate | 2022-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-2cdea64f1d2d4a559833c65202e9e30b2023-09-03T08:08:20ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-02-0112e000001110.1371/journal.pdig.0000011The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI).Julian Euma Ishii-RousseauShion SeinoDaniel K EbnerMaryam VarethMing Jack PoLeo Anthony CeliThe application of machine learning and artificial intelligence to clinical settings for prevention, diagnosis, treatment, and the improvement of clinical care have been demonstrably cost-effective. However, current clinical AI (cAI) support tools are predominantly created by non-domain experts and algorithms available in the market have been criticized for the lack of transparency behind their creation. To combat these challenges, the Massachusetts Institute of Technology Critical Data (MIT-CD) consortium, an affiliation of research labs, organizations, and individuals that contribute to research in and around data that has a critical impact on human health, has iteratively developed the "Ecosystem as a Service (EaaS)" approach, providing a transparent education and accountability platform for clinical and technical experts to collaborate and advance cAI. The EaaS approach provides a range of resources, from open-source databases and specialized human resources to networking and collaborative opportunities. While mass deployment of the ecosystem still faces several hurdles, here we discuss our initial implementation efforts. We hope this will promote further exploration and expansion of the EaaS approach, while also informing or realizing policies that will accelerate multinational, multidisciplinary, and multisectoral collaborations in cAI research and development, and provide localized clinical best practices for equitable healthcare access.https://doi.org/10.1371/journal.pdig.0000011 |
| spellingShingle | Julian Euma Ishii-Rousseau Shion Seino Daniel K Ebner Maryam Vareth Ming Jack Po Leo Anthony Celi The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI). PLOS Digital Health |
| title | The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI). |
| title_full | The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI). |
| title_fullStr | The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI). |
| title_full_unstemmed | The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI). |
| title_short | The "Ecosystem as a Service (EaaS)" approach to advance clinical artificial intelligence (cAI). |
| title_sort | ecosystem as a service eaas approach to advance clinical artificial intelligence cai |
| url | https://doi.org/10.1371/journal.pdig.0000011 |
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