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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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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.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Related Experiment Video

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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Evaluating GPT-5 for Melanoma Detection Using Dermoscopic Images.

Qingguo Wang1, Ihunna Amugo2, Harshana Rajakaruna3

  • 1Department of Biochemistry, Cancer Biology, Neurosciences and Pharmacology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA.

Diagnostics (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

Large language models like GPT-5 show promise for early melanoma detection. GPT-5 demonstrated improved diagnostic accuracy, especially in differential diagnoses, outperforming previous GPT-4 models.

Keywords:
ChatGPTGPT-5dermoscopylarge language modelmelanoma diagnosis

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Area of Science:

  • Artificial Intelligence
  • Dermatology
  • Medical Imaging

Background:

  • Melanoma is a deadly skin cancer where early detection is critical for survival.
  • Artificial intelligence (AI), specifically large language models (LLMs), offers potential for improving early melanoma detection.
  • Systematic assessment of LLMs, such as GPT-5, for melanoma detection is lacking.

Purpose of the Study:

  • To evaluate the diagnostic performance of GPT-5 on dermoscopic images for melanoma detection.
  • To compare GPT-5's capabilities against previous GPT-4 models in classifying skin lesions.

Main Methods:

  • GPT-5 was tested on 600 dermoscopic images from the ISIC Archive and HAM10K datasets.
  • The model performed three diagnostic tasks: primary diagnosis, top-3 differential diagnoses, and malignancy discrimination.
  • Performance was measured using sensitivity, specificity, accuracy, and F1 score against histopathology-verified ground truth.

Main Results:

  • GPT-5 showed modest accuracy for primary diagnosis but significantly improved performance for differential diagnoses (sensitivity >93%, specificity >86%).
  • For malignancy discrimination, GPT-5 exhibited more balanced sensitivity and specificity compared to GPT-4 variants.
  • GPT-5 outperformed GPT-4 models in differential diagnosis accuracy and overall classification reliability.

Conclusions:

  • GPT-5 demonstrates significant potential as a clinical decision support tool in dermatology, surpassing GPT-4 derivatives in differential diagnosis.
  • The model's ability to aid medical education in melanoma detection is highlighted.
  • Caution is advised due to GPT-5's tendency to misclassify melanoma as benign, necessitating further refinement.