Psoriatic arthritis (PsA), an immune-mediated chronic inflammatory skin and joint disease, affects approximately 0.27% of the adult population, and 20% of patients with psoriasis. Up to 10% of psoriasis patients are estimated for having undiagnosed PsA. Early diagnosis and treatment can prevent irreversible joint damage, disability and deformity. Questionnaires for screening to identify undiagnosed PsA patients require patient and physician involvement. To evaluate a proprietary machine learning tool (PredictAI™) developed for identification of undiagnosed PsA patients 1–4 years prior to the first time that they were suspected of having PsA (reference event). This retrospective study analyzed data of the adult population from Maccabi Healthcare Service between 2008 and 2020. We created 2 cohorts: The general adult population (“GP Cohort”) including patients with and without psoriasis and the Psoriasis cohort (“PsO Cohort”) including psoriasis patients only. Each cohort was divided into two non-overlapping train and test sets. The PredictAI™ model was trained and evaluated with 3 years of data predating the reference event by at least one year. Receiver operating characteristic (ROC) analysis was used to investigate the performance of the model, built using gradient boosted trees, at different specificity levels. Overall, 2096 patients met the criteria for PsA. Undiagnosed PsA patients in the PsO cohort were identified with a specificity of 90% one and four years before the reference event, with a sensitivity of 51% and 38%, and a PPV of 36.1% and 29.6%, respectively. In the GP cohort and with a specificity of 99% and for the same time windows, the model achieved a sensitivity of 43% and 32% and a PPV of 10.6% and 8.1%, respectively. The presented machine learning tool may aid in the early identification of undiagnosed PsA patients, and thereby promote earlier intervention and improve patient outcomes.

Artificial intelligence (AI), especially large language models (LLMs) like ChatGPT, has disrupted different medical disciplines, including dermatology. This review explores the application of ChatGPT in dermatological diagnosis, emphasizing its role in natural language processing (NLP) for clinical data interpretation, differential diagnosis assistance, and patient communication enhancement. ChatGPT can enhance a diagnostic workflow when paired with image analysis tools, such as convolutional neural networks (CNNs), by merging text and image data. While it boasts great capabilities, it still faces some issues, such as its inability to perform any direct image analyses and the risk of inaccurate suggestions. Ethical considerations, including patient data privacy and the responsibilities of the clinician, are discussed. Future perspectives include an integrated multimodal model and AI-assisted framework for diagnosis, which shall improve dermatology practice.

Dynamic translation is a general purpose tool used for instrumenting programs at run time. Many current translators perform substantial rewriting during translation in an attempt to reduce execution time. When dynamic translation is used as a ubiquitous policy enforcement mechanism, the majority of program executions have no dominating inner loop that can be used to amortize the cost of translation. Even under more favorable usage assumptions, our measurements show that such optimizations offer no significant benefit in most cases. A simpler, more maintainable, adaptable, and smaller translator may be preferable to more complicated designs. In this paper, we present HDTrans, a light-weight IA-32 to IA-32 binary translation system that uses some simple and effective translation techniques in combination with established trace linearization and code caching optimizations. We also present an evaluation of translation overhead under non-ideal conditions, showing that conventional benchmarks do not provide a good prediction of translation overhead when used pervasively. A further contribution of this paper is an analysis of the effectiveness of post-compile static pre-translation techniques for overhead reduction. Our results indicate that static pre-translation is effective only when expensive instrumentation or optimization is performed, and that efficient reload of pre-translated code incurs a substantial execution-time penalty.