Journal of the American Academy of Dermatology
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J. Am. Acad. Dermatol. · Aug 2020
ReviewCosmetic treatment in patients with autoimmune connective tissue diseases: Best practices for patients with morphea/systemic sclerosis.
Morphea and systemic sclerosis are inflammatory, sclerosing disorders. Morphea primarily affects the dermis and subcutaneous fat, while systemic sclerosis typically involves the skin and internal organs. Functional impairment and cosmetic disfigurement are common in both diseases. ⋯ There is scarce information to guide safety decisions regarding laser parameters, soft tissue augmentation, treatment intervals, and the concurrent use of immune-modifying or immune-suppressing medications. Given the fears of disease reactivation and exacerbation from postprocedural inflammation along with limited data, it is difficult for clinicians to provide evidence-based cosmetic treatment with realistic expectations with regard to short- and long-term outcomes. In the first article in this continuing medical education series, we attempt to address this practice gap.
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J. Am. Acad. Dermatol. · May 2020
ReviewDeep learning for dermatologists: Part I. Fundamental concepts.
Artificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose certain skin cancers from clinical photographs with the accuracy of an expert dermatologist. ⋯ Although experts will never be replaced by artificial intelligence, it will certainly affect the specialty of dermatology. In this first article of a 2-part series, the basic concepts of deep learning will be reviewed with the goal of laying the groundwork for effective communication between clinicians and technical colleagues. In part 2 of the series, the clinical applications of deep learning in dermatology will be reviewed and limitations and opportunities will be considered.
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J. Am. Acad. Dermatol. · May 2020
ReviewDeep learning for dermatologists: Part II. Current applications.
Because of a convergence of the availability of large data sets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning has been particularly impactful for medical image analysis. ⋯ In this second article of a 2-part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.
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Psoriasis is a chronic inflammatory disease with clinical manifestations of the skin that affect adults and children. In adults, biologics have revolutionized the treatment of moderate to severe plaque psoriasis where clear or almost clear is a tangible goal. Research on biologics has recently been extended to children. The introduction of these new therapeutic options has outpaced the limited guidelines in this population. ⋯ A treatment algorithm for moderate to severe plaque psoriasis in pediatric patients is presented, incorporating approved biologics. Guidance on baseline screening and ongoing monitoring is also provided. Ultimately, treatment choice depends on the patient and his or her caregiver, with consideration of comorbidities, impact on quality of life, and relevant safety aspects.
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J. Am. Acad. Dermatol. · Dec 2019
ReviewVisual perception, cognition, and error in dermatologic diagnosis: Key cognitive principles.
Dermatologic diagnosis relies on vision primarily and auditory and verbal input secondarily. Accurate dermatologic diagnosis is predicated on seeing and perceiving a skin finding, categorizing and naming the finding correctly, and comparing the visual data and data obtained from the totality of the clinical encounter (ie, from other sensory modalities) with one's working mental database of dermatologic diagnoses. ⋯ In part 1 of this 2-part report, we describe the pitfalls associated with visual recognition. In part 2, we discuss cognitive heuristics as they relate to the dermatologic diagnostic process and prevention of diagnostic error.