Transforming healthcare with AI: The impact in Dermatology

Due to its extensive clinical, dermatoscopic, and dermato-pathological image database, dermatology has assumed the lead position for implementing AI in the medical industry. The application of AI-based solutions can significantly impact the early and accurate detection of severe dermatoses. Dermatoses are a common condition seen by many patients of general and family practitioners. However, they cannot recognize severe cases due to their lack of dermatology training and specialization.

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At the histological level, AI has shown a lot of development in skin cancer diagnosis. A total of 695 lesions were examined by Hekler et al., each of which was categorized by a skilled histopathologist by current standards. A convolutional neural network (CNN) was trained using 595 of the final images, and after 11 test runs, it had a sensitivity/specificity/accuracy of 76%/60%/68%. Comparatively, the average sensitivity, specificity, and accuracy for the 11 pathologists were 51.8%/66.5%/59.2%, respectively.

As a result, scientists came to the conclusion that CNN had the potential to aid in the diagnosis of human melanoma because it had outperformed 11 histopathologists in the classification of histological melanoma images. Although still in its early stages of development, the AI dermatology diagnosis for skin cancer through clinical photos, dermatoscopic images, and histological images has much potential.

Furthermore, Google, the technology leader in AI, released an AI-powered dermatology assist tool that helps you understand what’s going on with issues related to your body’s largest organ: your skin, hair, and nails. The AI tool fine-tuned the model with identified data encompassing around 65,000 images and case data of diagnosed skin conditions, millions of curated skin concern images, and thousands of examples of healthy skin across different demographics. The research effort by Google validates the application of AI on the scale in dermatology.

The long-term treatment of chronic dermatoses can benefit from the use of AI. For instance, individuals undergoing surgery for skin cancer should visit the doctor frequently to assess the procedure’s results. AI can offer the necessary tool for remote comforting so that follow-up may be made effectively and affordably.

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The use of artificial intelligence in dermatology is rapidly increasing. It can completely transform patient treatment, especially in terms of increasing the sensitivity and precision of screening for skin lesions, including cancers. However, clinical and photographic data of all skin types are required for complex research, and the data must be produced through improved worldwide skin imaging collaboration.

In conclusion, doctors shouldn’t consider artificial intelligence’s modern approach a threat to their expertise; instead, it may supplement clinical practice in the future. Working knowledge of AI principles will enable dermatologists to provide improved diagnosis and treatment.

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