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  • Tschandl, P. (author)

Human-computer collaboration for skin cancer recognition

  • Article/chapterEnglish2020

Publisher, publication year, extent ...

  • 2020-06-22
  • Springer Science and Business Media LLC,2020

Numbers

  • LIBRIS-ID:oai:gup.ub.gu.se/294864
  • https://gup.ub.gu.se/publication/294864URI
  • https://doi.org/10.1038/s41591-020-0942-0DOI

Supplementary language notes

  • Language:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • 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 and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Rinner, C. (author)
  • Apalla, Z. (author)
  • Argenziano, G. (author)
  • Codella, N. (author)
  • Halpern, A. (author)
  • Janda, M. (author)
  • Lallas, A. (author)
  • Longo, C. (author)
  • Malvehy, J. (author)
  • 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)
  • Puig, S. (author)
  • Rosendahl, C. (author)
  • Soyer, H. P. (author)
  • Zalaudek, I. (author)
  • Kittler, H. (author)
  • Göteborgs universitetInstitutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi (creator_code:org_t)

Related titles

  • In:Nature Medicine: Springer Science and Business Media LLC26, s. 1229-12341078-89561546-170X

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