Comorbid Conditions Associated with Alopecia Areata: A Systematic Review and Meta-analysis.

BACKGROUND: Alopecia areata (AA) is a complex autoimmune condition resulting in nonscarring hair loss. In recent years, many studies have provided new evidence on comorbid diseases present in patients with AA. However, some studies have conflicting results, and analyses conducting a comprehensive approach are lacking. OBJECTIVE: The aim of our study was to provide an updated systematic review and meta-analysis of medical comorbidities associated with AA. METHODS: We searched PubMed, Embase, and Web of Science for case-control, cross-sectional, and cohort studies investigating medical comorbidities in AA published from inception through 1 February 2023. RESULTS: We screened 3428 abstracts and titles and reviewed 345 full text articles for eligibility. Ultimately, 102 studies were analyzed, comprising 680,823 patients with AA and 72,011,041 healthy controls. Almost all included studies (100 of 102 studies) were of satisfactory to high quality (Newcastle-Ottawa scale score ≥ 4). Among patients with AA, comorbidities with the highest odds ratios (OR) compared with healthy controls and data available from more than one study included vitamin D deficiency (OR 10.13, 95% CI 4.24-24.20), systemic lupus erythematous (OR 5.53, 95% CI 3.31-9.23), vitiligo (OR 5.30, 95% CI 1.86-15.10), metabolic syndrome (OR 5.03, 95% CI 4.18-6.06), and Hashimoto's thyroiditis (OR 4.31, 95% CI 2.51-7.40). AA may be a protective factor for certain disorders, for which the AA group had lower odds compared with healthy controls, such as irritable bowel syndrome (OR 0.38, 95% CI 0.14-0.99) and colorectal cancer (OR 0.61, 95% CI 0.42-0.89). CONCLUSION: These findings corroborate and contextualize the risks across comorbidities for patients with AA. Further work should be done to identify the underlying pathophysiology and understand appropriate screening criteria.

Skin phototype of participants in laser and light treatments of cosmetic dermatologic conditions: A systematic review.

BACKGROUND: In cosmetic dermatology, lasers and lights treat a variety of hair and skin conditions, including some that disproportionately affect people of color. AIMS: Our systematic review aims to understand the representation of participants with skin phototypes 4-6 in cosmetic dermatologic trials studying laser and light devices. METHODS: A systematic literature search was conducted using search terms "laser," "light," and multiple laser and light subtypes in the PubMed and Web of Science databases. All randomized controlled trials (RCTs) published between January 1, 2010 and October 14, 2021 that studied laser or light devices for cosmetic dermatologic conditions were eligible for inclusion. RESULTS: Our systematic review included 461 RCTs representing 14 763 participants. Of 345 studies that reported skin phototype, 81.7% (n = 282) included participants of skin phototypes 4-6, but only 27.5% (n = 95) included participants of skin phototypes 5 or 6. This trend of excluding darker skin phototypes persisted when results were stratified by condition, laser of study, study location, journal type, and funding source. CONCLUSIONS: Trials studying lasers and lights for the treatment of cosmetic dermatologic conditions need better representation of skin phototypes 5 and 6.

Clinical Application of Artificial Intelligence for Non-melanoma Skin Cancer.

OPINION STATEMENT: The development and implementation of artificial intelligence is beginning to impact the care of dermatology patients. Although the clinical application of AI in dermatology to date has largely focused on melanoma, the prevalence of non-melanoma skin cancers, including basal cell and squamous cell cancers, is a critical application for this technology. The need for a timely diagnosis and treatment of skin cancers makes finding more time efficient diagnostic methods a top priority, and AI may help improve dermatologists' performance and facilitate care in the absence of dermatology expertise. Beyond diagnosis, for more severe cases, AI may help in predicting therapeutic response and replacing or reinforcing input from multidisciplinary teams. AI may also help in designing novel therapeutics. Despite this potential, enthusiasm in AI must be tempered by realistic expectations regarding performance. AI can only perform as well as the information that is used to train it, and development and implementation of new guidelines to improve transparency around training and performance of algorithms is key for promoting confidence in new systems. Special emphasis should be placed on the role of dermatologists in curating high-quality datasets that reflect a range of skin tones, diagnoses, and clinical scenarios. For ultimate success, dermatologists must not be wary of AI as a potential replacement for their expertise, but as a new tool to complement their diagnostic acumen and extend patient care.

Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study.

Importance: The use of artificial intelligence (AI) is expanding throughout the field of medicine. In dermatology, researchers are evaluating the potential for direct-to-patient and clinician decision-support AI tools to classify skin lesions. Although AI is poised to change how patients engage in health care, patient perspectives remain poorly understood. Objective: To explore how patients conceptualize AI and perceive the use of AI for skin cancer screening. Design, Setting, and Participants: A qualitative study using a grounded theory approach to semistructured interview analysis was conducted in general dermatology clinics at the Brigham and Women's Hospital and melanoma clinics at the Dana-Farber Cancer Institute. Forty-eight patients were enrolled. Each interview was independently coded by 2 researchers with interrater reliability measurement; reconciled codes were used to assess code frequency. The study was conducted from May 6 to July 8, 2019. Main Outcomes and Measures: Artificial intelligence concept, perceived benefits and risks of AI, strengths and weaknesses of AI, AI implementation, response to conflict between human and AI clinical decision-making, and recommendation for or against AI. Results: Of 48 patients enrolled, 26 participants (54%) were women; mean (SD) age was 53.3 (21.7) years. Sixteen patients (33%) had a history of melanoma, 16 patients (33%) had a history of nonmelanoma skin cancer only, and 16 patients (33%) had no history of skin cancer. Twenty-four patients were interviewed about a direct-to-patient AI tool and 24 patients were interviewed about a clinician decision-support AI tool. Interrater reliability ratings for the 2 coding teams were κ = 0.94 and κ = 0.89. Patients primarily conceptualized AI in terms of cognition. Increased diagnostic speed (29 participants [60%]) and health care access (29 [60%]) were the most commonly perceived benefits of AI for skin cancer screening; increased patient anxiety was the most commonly perceived risk (19 [40%]). Patients perceived both more accurate diagnosis (33 [69%]) and less accurate diagnosis (41 [85%]) to be the greatest strength and weakness of AI, respectively. The dominant theme that emerged was the importance of symbiosis between humans and AI (45 [94%]). Seeking biopsy was the most common response to conflict between human and AI clinical decision-making (32 [67%]). Overall, 36 patients (75%) would recommend AI to family members and friends. Conclusions and Relevance: In this qualitative study, patients appeared to be receptive to the use of AI for skin cancer screening if implemented in a manner that preserves the integrity of the human physician-patient relationship.