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Related Concept Videos

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
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Related Experiment Video

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Human-computer collaboration for skin cancer recognition.

Philipp Tschandl1, Christoph Rinner2, Zoe Apalla3

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Artificial intelligence (AI) support in telemedicine improves diagnostic accuracy, especially for less experienced clinicians. However, faulty AI can mislead all medical professionals, highlighting the need for careful implementation in healthcare.

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Area of Science:

  • Medical Artificial Intelligence
  • Digital Health
  • Clinical Decision Support

Background:

  • Telemedicine adoption is rapidly increasing, necessitating integration of AI diagnostic tools.
  • Advances in AI for medical image analysis, particularly skin cancer detection, present new opportunities and risks.
  • Understanding AI's impact on clinical workflows and expertise levels is crucial.

Purpose of the Study:

  • To evaluate the effects of AI-based decision support on diagnostic accuracy across varying clinical expertise.
  • To compare different AI representation methods (multiclass probabilities vs. CBIR) in a mobile setting.
  • To assess AI utility in simulated telemedicine triage and second opinion scenarios.

Main Methods:

  • Simulated clinical workflows incorporating AI support for skin cancer diagnosis.
  • Comparison of diagnostic accuracy between AI alone, physicians alone, and AI-assisted physicians.
  • Evaluation of AI-based multiclass probabilities versus content-based image retrieval (CBIR).
  • Analysis of AI class-activation maps for diagnostic insights.

Main Results:

  • High-quality AI support enhanced diagnostic accuracy beyond AI or physicians alone.
  • Least experienced clinicians benefited most from AI-assisted decision-making.
  • AI-based multiclass probabilities outperformed CBIR in mobile environments.
  • AI demonstrated utility in simulated second opinions and telemedicine triage.
  • Faulty AI was found to mislead clinicians across all expertise levels.

Conclusions:

  • AI-based decision support holds significant potential to improve diagnostic accuracy, particularly for non-expert clinicians in telemedicine.
  • Careful development and validation of AI tools are essential to mitigate risks associated with faulty AI.
  • AI class-activation maps can offer valuable insights for improving human diagnosis and human-computer collaboration.