ACR's Connect and AI-LAB technical framework. Issue 4 (11th November 2022)
- Record Type:
- Journal Article
- Title:
- ACR's Connect and AI-LAB technical framework. Issue 4 (11th November 2022)
- Main Title:
- ACR's Connect and AI-LAB technical framework
- Authors:
- Brink, Laura
Coombs, Laura P
Kattil Veettil, Deepak
Kuchipudi, Kashyap
Marella, Sailaja
Schmidt, Kendall
Nair, Sujith Surendran
Tilkin, Michael
Treml, Christopher
Chang, Ken
Kalpathy-Cramer, Jayashree - Abstract:
- Abstract: Objective: To develop a free, vendor-neutral software suite, the American College of Radiology (ACR) Connect, which serves as a platform for democratizing artificial intelligence (AI) for all individuals and institutions. Materials and Methods: Among its core capabilities, ACR Connect provides educational resources; tools for dataset annotation; model building and evaluation; and an interface for collaboration and federated learning across institutions without the need to move data off hospital premises. Results: The AI-LAB application within ACR Connect allows users to investigate AI models using their own local data while maintaining data security. The software enables non-technical users to participate in the evaluation and training of AI models as part of a larger, collaborative network. Discussion: Advancements in AI have transformed automated quantitative analysis for medical imaging. Despite the significant progress in research, AI is currently underutilized in current clinical workflows. The success of AI model development depends critically on the synergy between physicians who can drive clinical direction, data scientists who can design effective algorithms, and the availability of high-quality datasets. ACR Connect and AI-LAB provide a way to perform external validation as well as collaborative, distributed training. Conclusion: In order to create a collaborative AI ecosystem across clinical and technical domains, the ACR developed a platform thatAbstract: Objective: To develop a free, vendor-neutral software suite, the American College of Radiology (ACR) Connect, which serves as a platform for democratizing artificial intelligence (AI) for all individuals and institutions. Materials and Methods: Among its core capabilities, ACR Connect provides educational resources; tools for dataset annotation; model building and evaluation; and an interface for collaboration and federated learning across institutions without the need to move data off hospital premises. Results: The AI-LAB application within ACR Connect allows users to investigate AI models using their own local data while maintaining data security. The software enables non-technical users to participate in the evaluation and training of AI models as part of a larger, collaborative network. Discussion: Advancements in AI have transformed automated quantitative analysis for medical imaging. Despite the significant progress in research, AI is currently underutilized in current clinical workflows. The success of AI model development depends critically on the synergy between physicians who can drive clinical direction, data scientists who can design effective algorithms, and the availability of high-quality datasets. ACR Connect and AI-LAB provide a way to perform external validation as well as collaborative, distributed training. Conclusion: In order to create a collaborative AI ecosystem across clinical and technical domains, the ACR developed a platform that enables non-technical users to participate in education and model development. Lay Summary: The American College of Radiology (ACR) has developed a free, vendor-neutral software suite called Connect that provides medical professionals the tools for local data analysis and artificial intelligence (AI) development. With AI-LAB, an application on Connect, users can import medical data, prepare the data, download public pre-trained models, develop new algorithms, and perform statistical evaluation, all while keeping their data safe behind their firewall. Connect and AI-LAB provide a powerful code-free, end-to-end pipeline to democratize AI for all. … (more)
- Is Part Of:
- JAMIA open. Volume 5:Issue 4(2022)
- Journal:
- JAMIA open
- Issue:
- Volume 5:Issue 4(2022)
- Issue Display:
- Volume 5, Issue 4 (2022)
- Year:
- 2022
- Volume:
- 5
- Issue:
- 4
- Issue Sort Value:
- 2022-0005-0004-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-11-11
- Subjects:
- artificial intelligence -- machine learning -- software design -- radiology -- data science
Medical informatics -- Periodicals
610.285 - Journal URLs:
- http://www.oxfordjournals.org/ ↗
https://academic.oup.com/jamiaopen ↗ - DOI:
- 10.1093/jamiaopen/ooac094 ↗
- Languages:
- English
- ISSNs:
- 2574-2531
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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