Digital skin imaging applications, part II: a comprehensive survey of post-acquisition image utilization features and technology standards.

BACKGROUND: Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications. MATERIALS AND METHODS: Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post-imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics. RESULTS: A total of 20 post-image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in-app content to perform a user-directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user-uploaded photos and fewer than 10% provided clear data use and privacy policies. CONCLUSION: Post-imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed.

Digital skin imaging applications, part I: Assessment of image acquisition technique features.

BACKGROUND: The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality. MATERIALS AND METHODS: Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), in-app connection to a healthcare provider (yes or no), and user type (patient, provider, or both). RESULTS: Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching. CONCLUSION: Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality.

The Model Minority Myth, Data Aggregation, and the Role of Medical Schools in Combating Anti-Asian Sentiment.

The COVID-19 pandemic has resulted in an alarming increase in hate incidents directed toward Asian Americans and Pacific Islanders (AAPIs), including verbal harassment and physical assault, spurring the nationwide #StopAsianHate movement. This rise in anti-Asian sentiment is occurring at a critical time of racial reckoning across the United States, galvanized by the Black Lives Matter movement, and of medical student calls for the implementation of antiracist medical curricula. AAPIs are stereotyped by the model minority myth, which posits that AAPIs are educated, hardworking, and therefore able to achieve high levels of success. This myth acts as a racial wedge between minorities and perpetuates harm that is pervasive throughout the field of medicine. Critically, the frequent aggregation of all AAPI subgroups as one monolithic community obfuscates socioeconomic and cultural differences across the AAPI diaspora while reinforcing the model minority myth. Here, the authors illustrate how the model minority myth and data aggregation have negatively affected the recruitment and advancement of diverse AAPI medical students, physicians, and faculty. Additionally, the authors discuss how data aggregation obscures health disparities across the AAPI diaspora and how the model minority myth influences the illness experiences of AAPI patients. Importantly, the authors outline specific actionable policies and reforms that medical schools can implement to combat anti-Asian sentiment and support the AAPI community.

Psychosocial issues and body dysmorphic disorder in aesthetics: Review and debate.

Body dysmorphic disorder (BDD) is a serious and debilitating psychiatric condition that disproportionately presents in dermatologic and cosmetic surgery patients. BDD is currently underrecognized in cosmetic medical settings and is thought to be underdiagnosed by behavioral health professionals. The significant comorbidities associated with this disorder, as well as potential harm done to both patient and physician, raise ethical and medicolegal concerns regarding its treatment. Although cosmetic interventions have historically been discouraged in BDD, recent studies have provided controversial evidence of benefit in certain cohorts. The rise of "snapchat dysmorphia" and the proposed explanatory phenomenon of perception drift have generated further debate around the de novo development or unmasking of BDD. We critically review and summarize existing debates around the treatment of BDD in cosmetic medicine. We provide guidance for screening, clinical interviewing, and the provision of psychoeducation in cases of suspected BDD.

Advances in the Etiology, Detection, and Clinical Management of Seborrheic Keratoses.

Seborrheic keratoses (SKs) are ubiquitous, generally benign skin tumors that exhibit high clinical variability. While age is a known risk factor, the precise roles of UV exposure and immune abnormalities are currently unclear. The underlying mechanisms of this benign disorder are paradoxically driven by oncogenic mutations and may have profound implications for our understanding of the malignant state. Advances in molecular pathogenesis suggest that inhibition of Akt and APP, as well as existing treatments for skin cancer, may have therapeutic potential in SK. Dermoscopic criteria have also become increasingly important to the accurate detection of SK, and other noninvasive diagnostic methods, such as reflectance confocal microscopy and optical coherence tomography, are rapidly developing. Given their ability to mimic malignant tumors, SK cases are often used to train artificial intelligence-based algorithms in the computerized detection of skin disease. These technologies are becoming increasingly accurate and have the potential to significantly augment clinical practice. Current treatment options for SK cause discomfort and can lead to adverse post-treatment effects, especially in skin of color. In light of the discontinuation of ESKATA in late 2019, promising alternatives, such as nitric-zinc and trichloroacetic acid topicals, should be further developed. There is also a need for larger, head-to-head trials of emerging laser therapies to ensure that future treatment standards address diverse patient needs.

Addressing Climate-Related Health Impacts During the Patient Encounter: A Practical Guide for Pediatric Dermatologists.

Pediatric populations are expected to bear most of the climate change impacts, with racial minorities and children living in poorer countries being particularly vulnerable. Given their relevance to cutaneous disease, dermatologists should be aware of these climate-sensitive health impacts and the ways in which they intersect with social factors. Strategies including targeted risk communication, motivational interviewing, and storytelling can help facilitate climate discussions during the patient encounter. In this article the authors summarize common dermatologic health impacts related to environmental exposures and provide sample scripts for climate messaging.

Assessment of published literature regarding skin of color in Pediatric Dermatology.

We sought to analyze the existence of skin of color (SOC)-related literature in Pediatric Dermatology. To do so, we applied criteria developed by Wilson et al (Assessment of skin of color and diversity and inclusion content of dermatologic published literature: an analysis and call to action. Int J Women Dermatol. 2021;15:26) to categorize SOC articles. We found that Pediatric Dermatology published 28.4% SOC articles in the last three years, higher than the average (16.8%) found across surveyed dermatology journals. Our findings demonstrate opportunity for improvement through the implementation of keyword standardization and continued prioritization of SOC-related content.

Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review.

IMPORTANCE: Clinical artificial intelligence (AI) algorithms have the potential to improve clinical care, but fair, generalizable algorithms depend on the clinical data on which they are trained and tested. OBJECTIVE: To assess whether data sets used for training diagnostic AI algorithms addressing skin disease are adequately described and to identify potential sources of bias in these data sets. DATA SOURCES: In this scoping review, PubMed was used to search for peer-reviewed research articles published between January 1, 2015, and November 1, 2020, with the following paired search terms: deep learning and dermatology, artificial intelligence and dermatology, deep learning and dermatologist, and artificial intelligence and dermatologist. STUDY SELECTION: Studies that developed or tested an existing deep learning algorithm for triage, diagnosis, or monitoring using clinical or dermoscopic images of skin disease were selected, and the articles were independently reviewed by 2 investigators to verify that they met selection criteria. CONSENSUS PROCESS: Data set audit criteria were determined by consensus of all authors after reviewing existing literature to highlight data set transparency and sources of bias. RESULTS: A total of 70 unique studies were included. Among these studies, 1 065 291 images were used to develop or test AI algorithms, of which only 257 372 (24.2%) were publicly available. Only 14 studies (20.0%) included descriptions of patient ethnicity or race in at least 1 data set used. Only 7 studies (10.0%) included any information about skin tone in at least 1 data set used. Thirty-six of the 56 studies developing new AI algorithms for cutaneous malignant neoplasms (64.3%) met the gold standard criteria for disease labeling. Public data sets were cited more often than private data sets, suggesting that public data sets contribute more to new development and benchmarks. CONCLUSIONS AND RELEVANCE: This scoping review identified 3 issues in data sets that are used to develop and test clinical AI algorithms for skin disease that should be addressed before clinical translation: (1) sparsity of data set characterization and lack of transparency, (2) nonstandard and unverified disease labels, and (3) inability to fully assess patient diversity used for algorithm development and testing.

Psychosocial Stress and Mechanisms of Skin Health: A Comprehensive Update.

Although the relationship between psychosocial stress and skin health is commonly invoked in both the scientific and popular literature, its underlying mechanisms are still not well understood. In this review, we provide a comprehensive update on the pathophysiology of stress and its clinical impact on skin homeostasis. The recent characterization of a bidirectional HPA stress axis in the skin has illuminated peripheral stress pathways, with effects spanning inflammation, atopy, barrier function, dermal thinning, wound healing, and melanogenesis. Additionally, new research into the cutaneous microbiome suggests the development of stress-induced dysbiosis through the “gut-brain-skin” axis. These new findings help contextualize how lifestyle factors such as diet, personal care practices, and sleep patterns may mediate and sometimes amplify the cutaneous impacts of psychological stress. We aim to clarify these clinically important relationships and highlight areas of future study that have widespread academic, clinical, and commercial implications. J Drugs Dermatol. 2021;20(1):62-29. doi:10.36849/JDD.5608.