JMIR dermatology最新文献

Unlabelled: Our study demonstrated the ability of ChatGPT-4 to answer 77.5% of all sampled text-based board review type questions correctly. Questions requiring the recall of factual information were answered correctly most often, with slight decreases in correctness as higher-order thinking requirements increased. Improvements to ChatGPT's visual diagnostics capabilities will be required before it can be used reliably for clinical decision-making and visual diagnostics.

Unlabelled: Delusional parasitosis is a rare psychotic disorder characterized by individuals firmly believing that they are infested with parasites despite no medical evidence. It may be shared among close contacts-termed folie à deux when 2 individuals are affected or folie à trois when 3 individuals share the delusion. Delusional parasitosis' somatic focus often leads patients to seek dermatologists, causing delayed diagnoses and unnecessary antiparasitic treatments. Herein, we present 2 familial cases of shared delusional parasitosis. In both cases, patients exhibited the matchbox sign, presenting nonparasitic materials as "evidence" of infestation. Dermatological and psychiatric evaluations excluded organic causes, diagnosing primary delusional parasitosis. Treatment with antipsychotic medications led to symptom remission. Psychoeducation was critical in preventing relapse in secondary cases. Delusional parasitosis with shared delusions is often misdiagnosed, requiring dermatologists to recognize it early. A multidisciplinary approach that combines psychiatric care and psychoeducation is essential for effective management and for preventing the reinforcement of delusional beliefs.

Unlabelled: This study analyzed over 2000 images of psoriasis across major web-based platforms and found a significant underrepresentation of darker skin tones, highlighting a critical gap in dermatologic representation that may contribute to misdiagnoses and health disparities among patients with skin of color.

Background: Artificial intelligence (AI) and telemedicine have great potential to transform dermatology care delivery, but patient perspectives on these technologies have not been systematically compared.

Objective: To examine patient perspectives on AI and telemedicine in dermatology to inform implementation strategies as these technologies increasingly converge in clinical practice.

Methods: A comprehensive literature search was conducted using PubMed, Scopus, and Embase databases between August 2024 and October 2024. We identified 48 articles addressing patient perspectives on AI and telemedicine in dermatology, with none directly comparing views on both technologies.

Results: Several distinct themes emerged regarding patient perspectives on these technologies: willingness to use, perceived benefits and risks, barriers to implementation, and conditions necessary for successful integration. Findings revealed that patients express hesitancy towards AI-based diagnoses that lack dermatologist involvement, while preferences for teledermatology varied by appointment reason, age, and prior technology exposure. Patients' motivations for AI implementation are connected to AI's potential for quicker diagnoses and improved triage efficiency, while telemedicine addresses logistical challenges such as reduced travel time and improved appointment availability. Both technologies were perceived to improve accessibility and diagnostic efficiency, though patients expressed concerns about AI's limited communication abilities and teledermatology's limits in performing physical examinations. Primary adoption barriers for these modalities included technological limitations and trust concerns, with patients emphasizing the need for dermatologist oversight, transparency, and adequate educational resources for successful integration.

Conclusions: The complementary strengths of AI and teledermatology suggest they could mitigate each other's limitations when integrated-AI potentially enhancing teledermatology's diagnostic accuracy while teledermatology addresses AI's lack of human connection. By thoroughly examining these perspectives, this review may serve as a guide for patient-centered technological integration in the future landscape of accessible dermatologic care.

Unlabelled: In our study, we developed a GPT assistant with a custom knowledge base for neurocutaneous diseases, tested its ability to answer common patient questions, and showed that a GPT using retrieval augmentation generation can improve the readability of patient educational material without being prompted for a specific reading level.

Background: Acne vulgaris (AV) is one of the most common skin disorders, with a peak incidence in adolescence and early adulthood. Topical treatments are usually used for mild to moderate AV; however, a lack of adherence to topical treatment is seen in patients due to various reasons. Therefore, personalized skincare recommendations may be beneficial for treating mild-to-moderate AV.

Objective: This study aimed to evaluate the effectiveness of a novel machine learning approach in predicting the optimal treatment for mild-to-moderate AV based on self-assessment and objective measures.

Methods: A randomized, evaluator-blinded, parallel-group study was conducted on 100 patients recruited from an internet-based database and randomized in a 1:1 ratio (groups A and B) based on their consent form submission. Groups A and B received customized product recommendations using a Bayesian machine learning model and self-selected treatments, respectively. The patients submitted self-assessed disease scores and photographs after the 8-week treatment. The primary and secondary outcomes were photograph evaluation by two board-certified dermatologists using the Investigator Global Assessment (IGA) scores and quality of life (QoL) measured using the Dermatology Life Quality Index (DLQI), respectively.

Results: Overall, 99 patients were screened, and 68 patients (mean age: 27 years, SD 4.56 years) were randomized into groups A (customized) and B (self-selected). IGA scores significantly improved after treatment in group A but not in group B (mean difference in IGA score; group A=0.32, P=.04 vs group B=0.09, P=.54). The DLQI significantly improved in group A from 7.75 at baseline to 3.5 (P<.001) after treatment but reduced in group B from 7.53 to 5.3 (P>.05). IGA scores and the DLQI were significantly correlated in group A, but not in group B. A total of 3 patients reported adverse reactions in group B, but none in group A.

Conclusions: Using a machine learning model for personalized skincare recommendations significantly reduced symptoms and improved severity and overall QoL of patients with mild-to-moderate AV, supporting the potential of machine learning-based personalized treatment options in dermatology.

Background: Atopic dermatitis (AD) is a chronic inflammatory skin condition affecting a significant percentage of the global population. Emerging research suggests a potential link between AD and neurodevelopmental disorders like attention-deficit/hyperactivity disorder (ADHD). However, there is a lack of comprehensive studies within the Saudi Arabian population examining this association.

Objective: This study aims to determine the prevalence of ADHD among patients with AD in Saudi Arabia and to explore potential associations with demographic and clinical factors.

Methods: In this cross-sectional, multicenter study conducted between May and November 2024, 419 patients with AD were recruited from various hospitals in Saudi Arabia. Children were screened for ADHD symptoms using the ADHD Rating Scale-5, while adults were assessed with the Adult Self-Report Scale. Logistic regression was used to evaluate the influence of AD severity, age, gender, nationality, and BMI on the likelihood of ADHD symptoms.

Results: A total of 419 patients with AD were included, of whom 234 (55.8%) were children and 185 (44.2%) were adults; 239 (57%) were female and 360 (85.9%) were Saudi nationals. ADHD symptoms were identified in 84 (20%) patients, with a slightly higher prevalence among children (49/234, 20.9%) compared to adults (35/185, 18.9%; P=.61). No significant associations were found between ADHD symptoms and gender, nationality, BMI, or AD severity in either age group. Moderate to severe AD was more common among adults (48/185, 25.9%) than children (42/234, 17.9%; P=.048).

Conclusions: This study found that 20% of patients with AD screened positive for ADHD symptoms, with slightly higher rates in children than adults. No significant associations were observed between ADHD symptoms and gender, nationality, BMI, or AD severity. Although no significant clinical predictors were identified, the findings emphasize the need for ADHD screening in patients with AD, particularly in regions with high AD prevalence. Future longitudinal studies should explore underlying mechanisms and assess how managing one condition may influence the other.

Unlabelled: Artificial intelligence (AI) is increasingly integrated into health care, offering potential benefits in patient education, triage, and administrative efficiency. This study evaluates AI-driven dialogue interfaces within an electronic health record and patient portal system for postoperative care in Mohs micrographic surgery.

Unlabelled: This letter highlights the increasing incidence of leishmaniasis cases in the United States, using the available data from Texas, and underscores the need for heightened awareness among health care providers regarding leishmaniasis diagnosis and prevention.

Unlabelled: TikTok's influence has significantly increased public interest in red-light therapy, surpassing that for traditional skin care treatments; this highlights the powerful role of social media in shaping health care trends and underscores the need for health care providers to stay informed about viral social media trends on treatment.