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Human-computer coll...
Human-computer collaboration for skin cancer recognition
- Article/chapterEnglish2020
Publisher, publication year, extent ...
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2020-06-22
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Springer Science and Business Media LLC,2020
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LIBRIS-ID:oai:gup.ub.gu.se/294864
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https://gup.ub.gu.se/publication/294864URI
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https://doi.org/10.1038/s41591-020-0942-0DOI
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
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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.
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Rinner, C.
(author)
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Apalla, Z.
(author)
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Argenziano, G.
(author)
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Codella, N.
(author)
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Halpern, A.
(author)
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Janda, M.
(author)
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Lallas, A.
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Longo, C.
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Malvehy, J.
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Paoli, John,1975Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi,Institute of Clinical Sciences, Department of Dermatology and Venereology(Swepub:gu)xpaojo
(author)
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Puig, S.
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Rosendahl, C.
(author)
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Soyer, H. P.
(author)
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Zalaudek, I.
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Kittler, H.
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Göteborgs universitetInstitutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi
(creator_code:org_t)
Related titles
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In:Nature Medicine: Springer Science and Business Media LLC26, s. 1229-12341078-89561546-170X
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- By the author/editor
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Tschandl, P.
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Rinner, C.
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Apalla, Z.
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Argenziano, G.
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Codella, N.
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Halpern, A.
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show more...
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Janda, M.
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Lallas, A.
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Longo, C.
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Malvehy, J.
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Paoli, John, 197 ...
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Puig, S.
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Rosendahl, C.
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Soyer, H. P.
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Zalaudek, I.
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Kittler, H.
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- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Biological Scien ...
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and Biochemistry and ...
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Nature Medicine
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University of Gothenburg