Artificial intelligence: Augmenting telehealth with large language models.

This brief editorial describes an emerging area of machine learning technology called large language models (LLMs). LLMs, such as ChatGPT, are the technological disruptor of this decade. They are going to be integrated into search engines (Bing and Google) and into Microsoft products in the coming months. They will therefore fundamentally change the way patients and clinicians access and receive information. It is essential that telehealth clinicians are aware of LLMs and appreciate their capabilities and limitations.

Motion Capture Quantification of User Variation in Topical Microparticle Application.

Motion capture has the potential to shed light on topical drug delivery application. This approach holds promise both as a training tool, and for the development of skin technology, but first, this approach requires validation. Elongated microparticles (EMP) are a physical delivery enhancement technology that relies on a user working in the microparticles using a textured applicator. We used this approach to test the hypothesis that motion capture data can be used to characterize the topical application process. Motion capture was used to record participants while applying a mixture of EMP and sodium fluorescein to ex-vivo porcine skin samples. Treated skin was assessed using reflectance confocal and fluorescence microscopy. Image analysis was used to quantify the microparticle density and the presence of a fluorescent drug surrogate, sodium fluorescein. A strong correlation was present between applicator motion and microparticle and drug delivery profiles. There were quantitative and qualitative differences in the intra- and inter- user application methods that went beyond the level of training. Frequency and velocity of the applicator motion were key factors that correlated with EMP density. Our quantitative analysis of an experimental dermatological device supports the hypothesis that self-application may benefit from some form of digital monitoring or training with feedback. Our conclusion is that the integration of motion capture into experimental dermatological research offers an improved and quantifiable perspective that could be broadly useful with respect to topical applications, and with respect to the instruction provided to patients and clinicians.