AI Article Synopsis
- - The incidence of melanoma is rising, but if caught early, it has a favorable prognosis, leading to a surge in developing AI-based image analysis systems for detecting skin lesions.
- - These systems utilize various AI algorithms for detecting melanoma, including both classic dermatoscopic image analysis and total body systems that assess the entire skin for moles and abnormalities.
- - The article evaluates the effectiveness of these AI applications, detailing their quantitative assessment, advantages, limitations, and identifying challenges that need addressing for better integration into dermatological practices.
Article Abstract
The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, such systems use various artificial intelligence (AI) algorithms. This area of computer science applications has recently undergone dynamic development, abounding in several solutions that are effective tools supporting diagnosticians in many medical specialties. In this contribution, a number of applications of different classes of AI algorithms for the detection of this skin melanoma are presented and evaluated. Both classic systems based on the analysis of dermatoscopic images as well as total body systems, enabling the analysis of the patient's whole body to detect moles and pathologic changes, are discussed. These increasingly popular applications that allow the analysis of lesion images using smartphones are also described. The quantitative evaluation of the discussed systems with particular emphasis on the method of validation of the implemented algorithms is presented. The advantages and limitations of AI in the analysis of lesion images are also discussed, and problems requiring a solution for more effective use of AI in dermatology are identified.
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| http://dx.doi.org/10.1016/j.clindermatol.2023.12.022 | DOI Listing |
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