Israel Medical Association Journal

Background: Identifying drug–drug interactions (DDIs) in dermatology can be cumbersome and time-consuming using traditional methods.

Objectives: To explore the potential of ChatGPT-4o, an artificial intelligence (AI)-based chatbot, to streamline the identification process.

Methods: ChatGPT-4o was tasked with assessing DDIs among commonly prescribed dermatological medications. The accuracy and reliability of the chatbot's outputs were compared against established references for 43 interactions.

Results: ChatGPT-4o successfully identified all evaluated interactions. It accurately described the interaction effects in 42 cases, with only one example of misdescription.

Conclusions: The findings highlight the potential of ChatGPT to serve as an effective and efficient alternative for identifying and understanding DDIs in dermatology, despite one noted error that emphasizes the need for ongoing verification against trusted references. Further research is needed to validate its use across a broader range of medications and clinical scenarios.

Psoriasis is a chronic, immune-mediated skin disease characterized by inflammatory lesions and systemic co-morbidities. Emerging evidence highlights the significant role of the microbiome in psoriasis pathogenesis. Dysbiosis of the skin and gut microbiota has been linked to increased disease severity and co-morbidities such as psoriatic arthritis and cardiovascular disease. In this review, we explored the microbiome's influence on immune responses in psoriasis and its potential as a therapeutic target. Microbial therapies, such as probiotics and fecal microbiota transplantation, hold promise for restoring microbial balance and improving outcomes. We also discuss how the microbiome modulates drug efficacy and toxicity, offering insights for personalized treatment approaches. While challenges remain in establishing causality and translating findings into clinical practice, leveraging the gut-skin axis may optimize psoriasis management and improve patient outcomes.