The Metaverse for Healthcare: Trends, Applications, and Future Directions of Digital Therapeutics for Urology

Factors affecting the adoption of metaverse in healthcare: The moderating role of digital division, and meta-culture

This research aims to find out the factors affecting the adoption of Metaverse in healthcare. This study explores the effect of perceived ease of use, perceived usefulness, and trust on adopting Metaverse in healthcare by keeping digital division and metaculture as moderating variables. The philosophical foundation is rooted in the positivism paradigm, the methodology is quantitative, and the approach used is deductive. Data was collected in Pakistan and China through judgmental sampling from 384 respondents. Partial Least Square Structural Equation Modelling (PLS-SEM) was used to analyze the collected data. The findings validate the relationship between perceived ease of use and the adoption of metaverse with β-value 0.236, t-value 5.207 and p-value 0.000, the relationship between perceived usefulness and the adoption of metaverse with β-value 0.233, t-value 4.017 and p-value 0.000, and the relationship between trust and adoption of a metaverse with β-value 0.192, t-value 3.589 and p-value 0.000. Results also show that the digital divide moderates the relation between perceived ease of use and adopting the metaverse having β-value 0.078, t-value 1.848 and p-value 0.032. Similarly, the findings also show that the digital divide does not moderate the relationships of perceived usefulness and trust with adopting the metaverse. Moreover, the meta culture also does not moderate the relationships of perceived ease of use, usefulness, and trust with adopting the metaverse. The study contributes to theoretical research on adopting a metaverse in healthcare by examining various factors necessary for its development. It also provides guidelines for the developers and adopters of suitable metaverse technology.

Emerging Trends in Artificial Intelligence-Based Urological Imaging Technologies and Practical Applications

The integration of artificial intelligence (AI) into medical imaging has notably expanded its significance within urology. AI applications offer a broad spectrum of utilities in this domain, ranging from precise diagnosis achieved through image segmentation and anomaly detection to improved procedural assistance in biopsies and surgical interventions. Although challenges persist concerning data security, transparency, and integration into existing clinical workflows, extensive research has been conducted on AI-assisted imaging technologies while recognizing their potential to reshape urological practices. This review paper outlines current AI techniques employed for image analysis to offer an overview of the latest technological trends and applications in the field of urology.

Designing Digital Therapeutic Content Using Chronic Disease Data: A Focus on Improving Urinary Dysfunction

In recent years, advancements in information and communication technologies, including artificial intelligence, big data, virtual reality, and augmented reality, have driven substantial growth in the field of digital medical diagnosis and treatment, thereby enhancing quality of life. Beginning in the mid-2010s with the advent of digital healthcare applications, and further accelerated by the impact of coronavirus disease 2019, digital therapeutic products have profoundly influenced society. Nevertheless, the expansion of digital therapeutics has encountered challenges associated with regulatory hurdles, differentiation from general digital healthcare, and the necessity for trustworthiness, which have contributed to a slower rate of progress. This study proposes a 3P content model-encompassing pre-education, prediction/diagnosis/treatment, and postmanagement-to increase the trustworthiness of digital therapeutics. The design of the 3P content model includes a fundamental structure that establishes networks with healthcare institutions, aiming to increase the reliability of data utilization and to facilitate integration with medical decision support systems. For case development, the study introduces a prototype of a mobile application that utilizes chronic disease urinary dysfunction data, demonstrating the cyclical structure inherent in the 3P content model.

Insights Into Korean Public Perspectives on Urology: Online News Data Analytics Through Latent Dirichlet Allocation Topic Modeling

PURPOSE

The objective of the study was to explore how urology-related news, one of the medical specialties profoundly linked to human health and life, is communicated to the public through media outlets that serves as primary sources of medical information for the public.

METHODS

In this study, articles were retrieved using the keyword 'Urology' from the Bigkinds spanning from January 1, 1990 to August 17, 2023. The Beautifulsoup library in Python was utilized for parsing the text to collect both titles and bodies of the articles. The gathered data was then analyzed using the latent Dirichlet allocation (LDA) algorithm from the scikit-learn library. Additionally, tools such as Wordcloud and Networkx were employed to visualize the relationships and patterns within the data.

RESULTS

The keyword analysis led to the identification of various themes in the articles, with a clear distinction between those providing medical information and those promoting healthcare services, technologies, and products. Notably, the content frequently intertwined informational aspects with promotional ones. Articles on men's health and pet diseases, for example, often combined educational material with product or procedure promotions. This overlap highlights the complexity of categorizing media content into distinct themes. Furthermore, the coverage of health insurance and treatment methods including recent advancements like robotic surgery reflected the evolving nature of healthcare discussions to emphasize the interplay between policy changes, medical advancements, and media portrayal.

CONCLUSION

By identifying 10 distinct topics mentioned in the news, the analysis determined which topics are common in urology-related news coverage. The findings revealed a substantial volume of medical information on urology in the media with a wide range of topics including treatment and prevention of urologic conditions, insurance information, new treatments, and news stories promoting new products or hospitals.