A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.
In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity f...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2015 - 2025 |
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| Autores principales: | , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515395&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515395 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515395 181515395 181515395 10.1007/s10278-024-01083-0 181515395 ppf: 2015 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey. aug: au: Vahdati, Sanaz Khosravi, Bardia Mahmoudi, Elham Zhang, Kuan Rouzrokh, Pouria Faghani, Shahriar Moassefi, Mana Tahmasebi, Aylin Andriole, Katherine P. Chang, Peter Farahani, Keyvan Flores, Mona G. Folio, Les Houshmand, Sina Giger, Maryellen L. Gichoya, Judy W. Erickson, Bradley J. affil: https://ror.org/02qp3tb03 Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, 55905, Rochester, MN, USA sug: subj: Artificial Intelligence Diagnostic Imaging Methods Medical Informatics Data Curation Medical Organizations Practice Guidelines Health Care Delivery Technology Data Quality Health Informatics Information Resources Research Personnel Health Personnel ab: In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks. pubtype: Academic Journal doctype: diagnostic images pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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