Search Results (4)

The 2025 avian influenza A(H5N1) outbreak has highlighted the urgent need for rapidly generated health communication materials during public health emergencies. Artificial intelligence (AI) systems offer transformative potential to accelerate content development pipelines while maintaining scientific accuracy and impact. We evaluated an AI-generated health communication material on bird flu precautions among 100 U.S. adults. The material was developed using ChatGPT for text generation based on CDC guidelines and Leonardo.AI for illustrations. Participants rated perceived message effectiveness, quality, realism, relevance, attractiveness, and visual informativeness. The AI-generated health communication material received favorable ratings across all dimensions: perceived message effectiveness (3.83/5, 77%), perceived message quality (3.84/5, 77%), realism (3.72/5, 74%), relevance (3.68/5, 74%), attractiveness (3.62/5, 74%), and visual informativeness (3.35/5 67%). Linear regression analysis revealed that all features significantly predicted perceived message effectiveness in unadjusted and adjusted models (p < 0.0001), e.g., multivariate analysis of outcome on perceived visual informativeness showed β = 0.51, 95% CI: 0.37–0.66, p < 0.0001. Also, mediation analysis revealed that visual informativeness accounted for 23.8% of the relationship between material attractiveness and perceived effectiveness. AI tools can enable real-time adaptation of prevention guidance during epidemiological emergencies while maintaining effective risk communication. Full article

Claim management is a critical process for insurance companies, requiring fairness, transparency, and efficiency to maintain policyholder trust and minimize financial impact. In our previous work, we introduced Insoore AI, an insurtech solution leveraging deep learning-based computer vision to automate car damage recognition and localization from user-provided pictures. While this approach demonstrated the potential of AI in claims management, it faced limitations in terms of performance and computational efficiency due to resource constraints. In this study, we present an improved version of Insoore AI, enabled by the High-Performance Computing (HPC) resources offered by the Booster module of LEONARDO HPC system located at the CINECA datacenter in Bologna, Italy. By leveraging the advanced computational capabilities of the above-mentioned HPC infrastructure, we trained larger and more complex deep learning models, processed higher-resolution images, and significantly reduced training and inference times. Our results show marked performance improvements in terms of damage detection, paving the way for more efficient, more effective and scalable claims management solutions. This work underscores the transformative potential of HPC resources in advancing AI-driven innovations in the insurance sector and is to be regarded as an improvement on the contribution of our previous work, enabled by relying on the DiffusionDet architecture and on a Swin Transformer backbone to solve the problem of automatic car damage detection and classification. Full article

Images, texts, voices, and signals can be synthesized by latent spaces in a multidimensional vector, which can be explored without the hurdles of noise or other interfering factors. In this paper, we present a practical use case that demonstrates the power of latent space in exploring complex realities such as image space. We focus on DaVinciFace, an AI-based system that explores the StyleGAN2 space to create a high-quality portrait for anyone in the style of the Renaissance genius Leonardo da Vinci. The user enters one of their portraits and receives the corresponding Da Vinci-style portrait as an output. Since most of Da Vinci’s artworks depict young and beautiful women (e.g., “La Belle Ferroniere”, “Beatrice de’ Benci”), we investigate the ability of DaVinciFace to account for other social categorizations, including gender, race, and age. The experimental results evaluate the effectiveness of our methodology on 1158 portraits acting on the vector representations of the latent space to produce high-quality portraits that retain the facial features of the subject’s social categories, and conclude that sparser vectors have a greater effect on these features. To objectively evaluate and quantify our results, we solicited human feedback via a crowd-sourcing campaign. Analysis of the human feedback showed a high tolerance for the loss of important identity features in the resulting portraits when the Da Vinci style is more pronounced, with some exceptions, including Africanized individuals. Full article

The rapid integration of Artificial Intelligence (AI) tools, specifically text-to-image generators, across various domains has had a profound impact on numerous fields. Despite this, the potential applications of AI image generators in architectural education, particularly in teaching the history of architecture, remain underexplored. This research aims to uncover the possibilities of utilizing AI image generators, with a specific focus on the capabilities of Leonardo AI, to enhance communication and engagement. This study employed an experimental methodology to investigate how the integration of AI image generators in education on the subject of “History of Architecture” promises to elevate the learning experience, offering new perspectives, visualizations, and interactive tools. Two workshops were conducted with university students to explore AI image generators’ potential applications in architectural history education. The first workshop utilized an iterative approach, while the second aimed to assess students’ analytical skills. The ultimate objective was to determine the capabilities of this tool and stimulate discussions regarding its potential future implementations. Following the workshops, online questionnaires were administered to students, and interviews were conducted with educators. The findings of this research underscore the need for validating AI-generated images, establishing guidelines to prevent misuse, and designing tailored AI tools for History of Architecture courses, thereby paving the way for further advancements in architectural history education. Full article