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WFRF:(Tschandl P.)
 

Sökning: WFRF:(Tschandl P.) > Human-computer coll...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00003921naa a2200541 4500
001oai:gup.ub.gu.se/294864
003SwePub
008240528s2020 | |||||||||||000 ||eng|
024a https://gup.ub.gu.se/publication/2948642 URI
024a https://doi.org/10.1038/s41591-020-0942-02 DOI
040 a (SwePub)gu
041 a eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
100a Tschandl, P.4 aut
2451 0a Human-computer collaboration for skin cancer recognition
264 c 2020-06-22
264 1b Springer Science and Business Media LLC,c 2020
520 a The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice. A systematic evaluation of the value of AI-based decision support in skin tumor diagnosis demonstrates the superiority of human-computer collaboration over each individual approach and supports the potential of automated approaches in diagnostic medicine.
650 7a NATURVETENSKAPx Biologix Biokemi och molekylärbiologi0 (SwePub)106022 hsv//swe
650 7a NATURAL SCIENCESx Biological Sciencesx Biochemistry and Molecular Biology0 (SwePub)106022 hsv//eng
653 a classification
653 a accuracy
653 a Biochemistry & Molecular Biology
653 a Cell Biology
653 a Research & Experimental
653 a Medicine
700a Rinner, C.4 aut
700a Apalla, Z.4 aut
700a Argenziano, G.4 aut
700a Codella, N.4 aut
700a Halpern, A.4 aut
700a Janda, M.4 aut
700a Lallas, A.4 aut
700a Longo, C.4 aut
700a Malvehy, J.4 aut
700a Paoli, John,d 1975u Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi,Institute of Clinical Sciences, Department of Dermatology and Venereology4 aut0 (Swepub:gu)xpaojo
700a Puig, S.4 aut
700a Rosendahl, C.4 aut
700a Soyer, H. P.4 aut
700a Zalaudek, I.4 aut
700a Kittler, H.4 aut
710a Göteborgs universitetb Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi4 org
773t Nature Medicined : Springer Science and Business Media LLCg 26, s. 1229-1234q 26<1229-1234x 1078-8956x 1546-170X
856u https://www.nature.com/articles/s41591-020-0942-0.pdf
8564 8u https://gup.ub.gu.se/publication/294864
8564 8u https://doi.org/10.1038/s41591-020-0942-0

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