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institution

Nuremberg Hospital

Germany

6 papers here198 citations across 6
fields
  • cs.CV2
  • cs.CR1
  • cs.SD1
  • eess.AS1
  • q-bio.QM1
ROR 010qwhr53OpenAlex

affiliations via OpenAlex

output
20222026
most citedDermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

176 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

  • Andreas Erzigkeit2 · h 2
  • Franziska Braun2 · h 5
  • H. Lehfeld2 · h 12
  • Korbinian Riedhammer2 · h 18
  • T. Hillemacher2 · h 46
  • Alexandra Arzberger1 · h 1
  • B. Oppermann1 · h 2
  • Jörg Keller1 · h 3
  • Markus Förstel1 · h 2
  • Maximilian Bundscherer1 · h 2
  • Ramin Tavakoli Kolagari1 · h 13
  • Sebastian P. Bayerl1 · h 12
collaborating institutions
  • Georg Simon Ohm University of Applied Sciences NurembergDE3 papers
  • Universitätsklinikum ErlangenDE3 papers
  • Paracelsus Medizinische PrivatuniversitätDE2 papers
  • Acibadem City ClinicBG1 paper
  • Al-Azhar UniversityEG1 paper
  • Burgas Free UniversityBG1 paper
  • Charité - Universitätsmedizin BerlinDE1 paper
  • Clínica AlemanaCL1 paper
  • Clinical Centre of KragujevacRS1 paper
  • Consorci Institut D'Investigacions Biomediques August Pi I SunyerES1 paper
  • Dubai HospitalAE1 paper
  • East Surrey HospitalGB1 paper
Showing q-bio.QMShow all

1 paper · 1 filter

q-bio.QM2023★ 176 cited

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Tirtha Chanda, Katja Hauser, Sarah Hobelsberger +32

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.