Big data requirements for artificial intelligence.
Purpose Of Review: To summarize how big data and artificial intelligence technologies have evolved, their current state, and next steps to enable future generations of artificial intelligence for ophthalmology.Recent Findings: Big data in health care is ever increasing in volume and variety, enabled...
| Publicado en: | Current Opinion in Ophthalmology Vol. 31; no. 5; pp. 318 - 324 |
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| Autores principales: | , , |
| Formato: | review Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Sep2020
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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=146014643&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146014643 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10408738 IQX jtl: Current Opinion in Ophthalmology issn: 10408738 maglogo: N pubinfo: dt: Sep2020 vid: 31 iid: 5 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 146014643 146014643 NLM32657996 146014643 10.1097/ICU.0000000000000676 NLM32657996 146014643 ppf: 318 ppct: 6 formats: tig: atl: Big data requirements for artificial intelligence. aug: au: Wang, Sophia Y. Pershing, Suzann Lee, Aaron Y. affil: Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California sug: subj: Artificial Intelligence Standards Ophthalmology Standards Clinical Assessment Tools Scales ab: Purpose Of Review: To summarize how big data and artificial intelligence technologies have evolved, their current state, and next steps to enable future generations of artificial intelligence for ophthalmology.Recent Findings: Big data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and artificial intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of artificial intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing artificial intelligence model architectures, and access to artificial intelligence models through open application program interfaces (APIs).Summary: Future requirements for big data and artificial intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of artificial intelligence by promoting standards for data labels, data sharing, artificial intelligence model architecture sharing, and accessible code and APIs. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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