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Human-computer coll...
Human-computer collaboration for skin cancer recognition
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Tschandl, P. (author)
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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. (author)
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Longo, C. (author)
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Malvehy, J. (author)
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- Paoli, John, 1975 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi,Institute of Clinical Sciences, Department of Dermatology and Venereology
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Puig, S. (author)
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Rosendahl, C. (author)
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Soyer, H. P. (author)
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Zalaudek, I. (author)
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Kittler, H. (author)
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(creator_code:org_t)
- 2020-06-22
- 2020
- English.
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In: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 26, s. 1229-1234
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https://www.nature.c...
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https://doi.org/10.1...
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Abstract
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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.
Subject headings
- NATURVETENSKAP -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
Keyword
- classification
- accuracy
- Biochemistry & Molecular Biology
- Cell Biology
- Research & Experimental
- Medicine
Publication and Content Type
- ref (subject category)
- art (subject category)
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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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show less...
- 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 ...
- Articles in the publication
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Nature Medicine
- By the university
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University of Gothenburg