Predicting psoriasis severity using machine learning: A systematic review.

In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from experience, have progressed past the diagnosis and classification of skin lesions. A lack of systematic reviews exists to explore the role of ML in predicting the severity of psoriasis. This systematic review aims to identify and summarize the existing literature on predicting psoriasis severity using ML algorithms and identify gaps in current clinical applications of these tools. OVID Embase, OVID MEDLINE, ACM Digital Library, Scopus, and IEEE Xplore were searched from inception to August, 2024. A total of 30 articles met our inclusion criteria and were included in this review. One article used serum biomarkers, while the remaining 29 used image-based models. The most common severity assessment score employed by these ML models was the Psoriasis Area Severity Index score, followed by Body Surface Area, with fifteen and five articles, respectively. The small size and heterogeneity of the existing literature are the primary limitations of this review. Progress in assessing skin lesion severity through ML in dermatology has advanced, but prospective clinical applications remain limited. ML and AI promise to improve psoriasis management, especially in non-image-based applications requiring further exploration. Large-scale prospective trials using diverse image datasets are necessary to evaluate and predict the clinical value of these predictive AI models.

Applications of Machine Learning in Prognostication of mTBI: A Systematic Review.

OBJECTIVE: To review the literature regarding the current state and clinical applicability of machine learning (ML) models in prognosticating the outcomes of patients with mild traumatic brain injury (mTBI) in the early clinical presentation. DESIGN: Databases were searched for studies including ML and mTBI from inception to March 10, 2023. Included studies had a primary outcome of predicting post-mTBI prognosis or sequalae. The Prediction model study Risk of Bias for Predictive Models assessment tool (PROBAST) was used for assessing the risk of bias and applicability of included studies. RESULTS: Out of 1235 articles, 10 met the inclusion criteria, including data from 127,929 patients. The most frequently used modeling techniques were Support Vector Machine (SVM) and Artificial Neural Network (NN) and Area Under the Curve (AUC) ranged from 0.66-0.889. Despite promise, several limitations to studies exist such as low sample sizes, database restrictions, inconsistencies in patient presentation definitions and lack of comparison to traditional clinical judgment or tools. CONCLUSION: ML models show potential in early stage mTBI prognostication, but to achieve widespread adoption, future clinical studies prognosticating mTBI using ML need to reduce bias, provide clarity and consistency in defining patient populations targeted, and validate against established benchmarks.

Diagnosing contact dermatitis using machine learning: A review.

BACKGROUND: Machine learning (ML) offers an opportunity in contact dermatitis (CD) research, where with full clinical picture, may support diagnosis and patch test accuracy. OBJECTIVE: This review aims to summarise the existing literature on how ML can be applied to CD in its entirety. METHODS: Embase, Medline, IEEE Xplore, and ACM Digital Library were searched from inception to February 7, 2024, for primary literature reporting on ML models in CD. RESULTS: 7834 articles were identified in the search, with 110 moving to full-text review, and six articles included. Two used ML to identify key biomarkers to help distinguish between allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD), three used image data to distinguish between ACD and ICD, and one used clinical and demographical data to predict the risk of positive patch tests. All studies used supervision in their ML model training with a total of 49 704 patients across all data sets. There was sparse reporting of the accuracy of these models. CONCLUSIONS: Although the available research is still limited, there is evidence to suggest that ML has potential to support diagnostic outcomes in a clinical setting. Further research on the use of ML in clinical practice is recommended.

A systematic review of the risk of cutaneous malignancy associated with ultraviolet nail lamps: what is the price of beauty?

Gel manicures have become part of a popular personal care service in the last two decades due to increased longevity of the polish and the added strength to the nail plate. Prolonged exposure to nail ultraviolet (UV) lamps is required to cure the gel polish. Despite the increased use of UV nail lamps, there is limited consensus in the literature on the risk of skin malignancy associated with UV nail lamps. The objective of this article was to provide a systematic review of the risk of skin malignancy associated with the use of UV nail lamps and to synthesize evidence-based recommendations on their safe usage. A systematic review of the literature was conducted on the databases, Medline and Embase, in accordance with PRISMA guidelines. The search yielded 2,331 non-duplicate articles. Nine were ultimately included, of which three were case reports, one was a cross-sectional study, and five were experimental studies. The risk of bias per the Joanna Briggs Institute guidelines was high or unclear, likely due to the number of case reports included. Prolonged and repeated exposure to UV nail lamps may pose a low risk of skin cancer. It is important to note that the available evidence is weak, and patients should be informed about the limited data to make their own decisions. Dermatologists and other healthcare providers should be updated with the latest evidence to address patients' concerns about gel manicures and suggest practices which can effectively reduce the risk of cutaneous malignancy associated with gel manicures, such as the use of UV-blocking gloves or properly applied sunscreens.

Solutions for Addressing the Dermatologist Shortage in Rural Canada: A Review of the Literature.

In Canada, there is a maldistribution of dermatologists, with as many as 5.6 dermatologists per 100,000 population in urban areas and as low as 0.6 per 100,000 in rural areas. Considering trends of dermatologists to work in group practices in urban areas, and the low number of rural dermatologists, one solution may be to incentivize dermatologists to practice rurally. Several solutions using the following themes are discussed: dermatology program-specific incentives, dermatology practice-specific incentives, and other indirect incentives. The low number of dermatologists in rural areas in Canada is concerning and has negative consequences for access to care for patients in rural areas, ultimately resulting in worse patient outcomes. Future research is needed to evaluate the impact of these initiatives and assess future access to dermatological care.