Various studies have been published on the remote assessment of eczema severity from digital camera images. Successful deployment of an accurate and robust AI-powered tool for such purposes can aid the formulation of eczema treatment plans and assist in patient monitoring. This review aims to provide an overview of the quality of published studies on this topic and to identify challenges and suggestions to improve the robustness and reliability of existing tools. We identified 25 articles from the Scopus database that aimed to assess eczema severity automatically from digital camera images by eczema area detection (n=13), which is important for prior delineation of the most relevant clinical features, and/or severity prediction (n=12). Deep learning methods (n=14) were more commonly used in recent years over conventional machine learning (n=11). A set of 20 pre-defined criteria were used for critical appraisal in this study. Study quality was hindered in many cases due to dataset challenges, with only 28% of studies reporting patient age range and 16% reporting skin phototype range. Furthermore, 52% of studies utilised solely non-public datasets and only 17% provided open-source access to code repositories, making validation of experimental results a significant challenge. In terms of algorithm design, attempts to improve model accuracy and process automation are widely reported. However, there remains limited implementation of methods for explicitly improving model trustworthiness and robustness. There is a need for a high-quality dataset with a sufficient number of bias-free images and consistent labels, as well as improved image analytics methods, to enhance the state of remote eczema severity assessment algorithms. Improving the interpretability and explainability of developed tools will further improve long-term reliability and trustworthiness.

Standard approaches for uncertainty quantification in deep learning and physics-informed learning have persistent limitations. Indicatively, strong assumptions regarding the data likelihood are required, the performance highly depends on the selection of priors, and the posterior can be sampled only approximately, which leads to poor approximations because of the associated computational cost. This paper introduces and studies confidence interval (CI) estimation for deterministic partial differential equations as a novel problem. That is, to propagate confidence, in the form of CIs, from data locations to the entire domain with probabilistic guarantees. We propose a method, termed Physics-Informed Confidence Propagation (PICProp), based on bi-level optimization to compute a valid CI without making heavy assumptions. We provide a theorem regarding the validity of our method, and computational experiments, where the focus is on physics-informed learning. Code is available at https://github.com/ShenQianli/PICProp.

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. Based on this new insight, we propose an improved version of Mixup, theoretically justified to deliver better generalization performance than the vanilla Mixup. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across multiple datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.