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Collective human intelligence outperforms artificial intelligence in a skin lesion classification task.

Julia K Winkler1, Katharina Sies1, Christine Fink1

  • 1Department of Dermatology, University of Heidelberg, Heidelberg, Germany.

Journal Der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG
|June 7, 2021
PubMed
Summary
This summary is machine-generated.

Collective human intelligence (CoHI) from 120 dermatologists significantly outperformed individual dermatologists and convolutional neural networks (CNNs) in classifying skin lesions. This finding highlights the power of group expertise in medical image diagnosis.

Keywords:
artificial intelligencecollectiveconvolution neural networkskin lesion classification

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Convolutional neural networks (CNNs) demonstrate high accuracy in medical image diagnosis, often matching individual physicians.
  • Collective human intelligence (CoHI) has shown potential to surpass individual diagnostic capabilities.

Purpose of the Study:

  • To compare the diagnostic performance of CoHI (120 dermatologists) against individual dermatologists and state-of-the-art CNNs.
  • To evaluate the accuracy of CoHI in a demanding skin lesion classification task.

Main Methods:

  • A cross-sectional reader study involved 120 dermatologists diagnosing 30 clinical cases.
  • Dermatoscopic images were classified by individual dermatologists, CoHI (majority vote), and binary/multiclass CNNs.
  • Diagnostic classifications were scored against ground truth for accuracy, sensitivity, and specificity.

Main Results:

  • CoHI achieved significantly higher accuracy (80.0%) than individual dermatologists (75.7%) and CNNs (70.0%) in binary classifications (P < 0.001).
  • CoHI demonstrated superior sensitivity (82.4%) and specificity (76.9%) compared to individuals and CNNs.
  • In multiclass evaluation, CoHI's diagnostic accuracy surpassed individual dermatologists, whose performance was comparable to multiclass CNNs.

Conclusions:

  • The majority vote of an interconnected group of dermatologists (CoHI) outperformed individual diagnoses and CNNs.
  • CoHI represents a powerful tool for improving diagnostic accuracy in complex medical image classification tasks.