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Article summary:

1. ScienceDirect.com has 472,404 search results for articles related to skin lesions.

2. The articles include research and review articles on topics such as skin lesion segmentation and classification, monitoring of pigmented skin lesions using 3D whole body imaging, and multi-modal deep learning frameworks for multi-label skin lesion classification.

3. The publications range from medical journals like the Journal of the American Academy of Dermatology to computer science journals like Computers in Biology and Medicine.

Article analysis:

The article titled "Skin lesion - Search | ScienceDirect.com" provides a comprehensive list of research articles and review articles related to skin lesion segmentation, classification, and monitoring. The search results are sorted by relevance and date, allowing readers to easily find the most recent and relevant studies.

One potential bias in this article is that it only includes studies published on ScienceDirect.com, which may not represent the entire body of research on skin lesions. Additionally, the search results are sorted by relevance and date, which could potentially prioritize certain studies over others based on their popularity or publication date rather than their scientific rigor or quality.

The article does not appear to contain any unsupported claims or promotional content. However, it is important to note that some of the studies listed may have been funded by pharmaceutical companies or other organizations with a vested interest in promoting certain treatments or technologies for skin lesions.

One potential missing point of consideration is the impact of socioeconomic factors on skin lesion diagnosis and treatment. For example, individuals from lower-income backgrounds may have less access to dermatologists or advanced imaging technologies, leading to disparities in care.

Overall, while this article provides a useful resource for researchers and healthcare professionals interested in skin lesion segmentation and classification, readers should be aware of potential biases in the selection and presentation of studies.