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Our study at Chi Mei Medical Center introduced "A+ Nurse," a ChatGPTbased LLM tool, into the nursing documentation process to enhance efficiency and accuracy. The tool offers optimized recording and critical reminders, reducing documentation time from 15 to 5 minutes per patient while maintaining record quality. Nurses appreciated the tool's intuitive design and its effectiveness in improving documentation. This successful integration of AI-generated content in healthcare illustrates the potential of AI to streamline processes and improve patient care, setting a precedent for future AI-driven healthcare innovations. [ABSTRACT FROM AUTHOR]

Abstract Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest level of bias, and ChatGPT is the sole model capable of declining content generation when provided with biased prompts.

The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs.

Drawn inspiration from phenomenal attributes and translating them into heuristic model tools is one of the effective means to promote architectural form innovation. However, over-reliance on perception indicates greater risks in decision-making. Nowadays, AI-generated Content (AIGC) technology combines the advantages of information comprehensiveness and modelling efficiency, providing new possibilities for the translation of architectural attributes. Based on attribute study, this paper proposes a new approach to spatial translation that uses the Generative Adversarial Network (VQGAN + CLIP) to realize the visualization of abstract concepts and then adds multi-dimensional influence through the Keyframe Style Transfer technology. The eclectic attribute is used as an example for the 2D and virtual 3D translation feasibility experiments. The article aims to improve the scientificity and influence of spatial translation through a technical organization from the perspective of architects. While providing an innovative, democratic and efficient aided-design tool also highlights a new angle for AIGC-involved pre-design. [ABSTRACT FROM AUTHOR]

Generating architectural layouts from sites to flats, encompassing site layouts (SLs), building layouts (BLs), and flat layouts (FLs), presents a complex process. Notably, the BL generation is challenging due to the small scale of data, making it difficult to train effective neural networks. This paper introduces an approach for generating layouts throughout the complete process. Initially, it proposes an enhanced generative adversarial network (GAN) combined with the transformer for Stable Diffusion (TranSD-GAN), considering design boundaries and requirements. Subsequently, for generating BLs with small-scale datasets, the paper proposes a stacking transfer learning method. Following this, image operations are conducted to support the flow of building information. Ultimately, BIM models are created at each stage. Through comparative experiments involving neural networks and generation cases, it is demonstrated that the proposed method significantly improves the generative capabilities of small-scale datasets and effectively aids designers throughout the layout design from sites to flats. • Architectural layouts from sites to flats are generated automatically. • An enhanced GAN with the transformer from Stable Diffusion is proposed. • Stacking transfer learning is proposed for a small-scale building layout dataset. • Image operations are performed to streamline building information flow. [ABSTRACT FROM AUTHOR]

The emerging digital tourism interpretations give rise to a novel intermediary for delivering interpretation in the form of professional-generated content (PGC) or artificial intelligence-generated content (AIGC). The pressing inquiry is whether artificial intelligence can surpass professional interpreters in terms of interpretation content quality, and if so, which specific dimensions it excels in. This study used a mixed-method approach encompassing grounded theory and content analysis. Grounded research, based on a pioneering digital tourism interpretation platform in China, unveiled three dimensions of content quality in digital tourism interpretation: informativeness , emotional appeal , and empathy. Furthermore, content analysis and ANOVA indicated that AIGC demonstrates comparatively lower levels of content quality than PGC across all three dimensions. These conclusions affirm that, regarding interpretation content quality, AI cannot replace human professional interpreters; however, considering AI's superior efficiency in generating interpretation content, the potential for human-machine collaboration in digital tourism interpretations is suggested. • Content generated by AI as digital tourism interpretations content. • Informativeness, emotional appeal, and empathy as content quality dimensions. • Generative AI empowering rather than replacing human interpreters. • Potential for human-machine collaboration within the tourism industry. [ABSTRACT FROM AUTHOR]

Our study at Chi Mei Medical Center introduced "A+ Nurse," a ChatGPT-based LLM tool, into the nursing documentation process to enhance efficiency and accuracy. The tool offers optimized recording and critical reminders, reducing documentation time from 15 to 5 minutes per patient while maintaining record quality. Nurses appreciated the tool's intuitive design and its effectiveness in improving documentation. This successful integration of AI-generated content in healthcare illustrates the potential of AI to streamline processes and improve patient care, setting a precedent for future AI-driven healthcare innovations.

Urban design is the process of designing and shaping the physical forms of cities, towns, and suburbs. It involves the arrangement and design of street systems, groups of buildings, public spaces, and landscapes, to make the urban environment performative and sustainable. The typical design process, reliant on manual work and expert experience has unavoidable low efficiency in generating high-performing design solutions due to the involvement of complex social, institutional, and economic contexts and the trade-off between conflicting preferences of different stakeholder groups. Taking advantage of artificial intelligence (AI) and computational capacity, generative urban design (GUD) has been developed as a trending technical direction to narrow the gaps and produce design solutions with high efficiency at early design stages. It uses computer-aided generative methods, such as evolutionary optimization and deep generative models, to efficiently explore complex solution spaces and automatically generate design options that satisfy conflicting objectives and various constraints. GUD experiments have attracted much attention from academia, practitioners, and public authorities in recent years. However, a systematic review of the current stage of GUD research is lacking. This study, therefore, reports on a systematic investigation of the existing literature according to the three key stages in the GUD process: (1) design problem formulation, (2) design option generation, and (3) decision-making. For each stage, current trends, findings, and limitations from GUD studies are examined. Future directions and potential challenges are discussed and presented. The review is highly interdisciplinary and involves articles from urban study, computer science, social science, management, and other fields. It reports what scholars have found in GUD experiments and organizes a diverse and complicated technical agenda into something accessible to all stakeholders. The results and discoveries will serve as a holistic reference for GUD developers and users in both academia and industry and form a baseline for the field of GUD development in the coming years. • Systematically review the process of how generative methods are applied in urban form generation. • Highly interdisciplinary that involves articles from urban study, computer science, social science, and other fields. • Provision of a summary of the current trends, findings, future directions, and potential challenges of GUD research. • Organize a diverse and complicated GUD technical agenda into something accessible to all stakeholders. • Serve as a holistic reference for GUD developers and users and form a baseline for GUD development in the coming years. [ABSTRACT FROM AUTHOR]