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A practical guide t...
A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
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Oettl, Felix C. (author)
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Pareek, Ayoosh (author)
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- Winkler, Philipp W. (author)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Johannes Kepler Universität Linz (JKU),Johannes Kepler University of Linz (JKU),Göteborgs universitet,University of Gothenburg
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- Zsidai, Bálint (author)
- Göteborgs universitet,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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Pruneski, James (author)
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- Senorski, Eric Hamrin (author)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Göteborgs universitet,University of Gothenburg
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Kopf, Sebastian (author)
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- Ley, Christophe (author)
- Université du Luxembourg,University of Luxembourg
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Herbst, Elmar (author)
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- Oeding, Jacob F. (author)
- Göteborgs universitet,University of Gothenburg
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Grassi, Alberto (author)
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- Hirschmann, Michael T. (author)
- Universität Basel,University of Basel
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Musahl, Volker (author)
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- Samuelsson, Kristian (author)
- Göteborgs universitet,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
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Tischer, Thomas (author)
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- Feldt, Robert, 1972 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Journal of Experimental Orthopaedics. - 2197-1153. ; 11:3
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Abstract
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- Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.
Subject headings
- SAMHÄLLSVETENSKAP -- Annan samhällsvetenskap -- Tvärvetenskapliga studier inom samhällsvetenskap (hsv//swe)
- SOCIAL SCIENCES -- Other Social Sciences -- Social Sciences Interdisciplinary (hsv//eng)
Keyword
- performance metrics
- AI
- digitalization
- ML
- healthcare
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- By the author/editor
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Oettl, Felix C.
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Pareek, Ayoosh
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Winkler, Philipp ...
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Zsidai, Bálint
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Pruneski, James
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Senorski, Eric H ...
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show more...
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Kopf, Sebastian
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Ley, Christophe
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Herbst, Elmar
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Oeding, Jacob F.
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Grassi, Alberto
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Hirschmann, Mich ...
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Musahl, Volker
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Samuelsson, Kris ...
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Tischer, Thomas
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Feldt, Robert, 1 ...
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- About the subject
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- SOCIAL SCIENCES
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SOCIAL SCIENCES
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and Other Social Sci ...
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and Social Sciences ...
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Journal of Exper ...
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Chalmers University of Technology