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Presenting artifici...
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Olczak, JakubKarolinska Institutet,Karolinska Inst, Sweden
(författare)
Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders : an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal
- Artikel/kapitelEngelska2021
Förlag, utgivningsår, omfång ...
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2021-05-14
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Taylor & Francis,2021
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electronicrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:liu-175976
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175976URI
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https://doi.org/10.1080/17453674.2021.1918389DOI
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https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-195101URI
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http://kipublications.ki.se/Default.aspx?queryparsed=id:146582502URI
Kompletterande språkuppgifter
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:art swepub-publicationtype
Anmärkningar
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Funding Agencies|Region Stockholm (ALF project); Karolinska InstituteKarolinska Institutet
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Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research. Methods and results - We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing. Interpretation - We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Pavlopoulos, JohnStockholm Univ, Sweden
(författare)
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Prijs, JasperFlinders Univ S Australia, Australia; Univ Groningen, Netherlands
(författare)
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Ijpma, Frank F. A.Machine Learning Consortium, Linkoping, Sweden; Univ Groningen, Netherlands
(författare)
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Doornberg, Job N.Machine Learning Consortium, Linkoping, Sweden; Flinders Univ S Australia, Australia; Univ Groningen, Netherlands
(författare)
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Lundström, ClaesLinköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV(Swepub:liu)clalu03
(författare)
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Hedlund, JoelLinköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten,Nationellt superdatorcentrum (NSC)(Swepub:liu)joehe05
(författare)
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Gordon, MaxKarolinska Institutet,Karolinska Inst, Sweden
(författare)
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Pavlopoulos, IoannisStockholms universitet,Institutionen för data- och systemvetenskap(Swepub:su)iopa3492
(författare)
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Karolinska InstitutetKarolinska Inst, Sweden
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Acta Orthopaedica: Taylor & Francis92:5, s. 513-5251745-36741745-3682
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