Abstract
The research proposal sheds light on the achievements in the healthcare industry that are made possible by the integration of metaverse technologies and artificial intelligence, hence presenting a vast number of challenges to be addressed and opportunities to be tapped. The main focus of the study is based on electronic medical record information generation and medical image data analysis made possible by technological advancements within the medical landscape. The study outlines three main research questions, which, through a rapid review-based approach, are intended to solve the challenges and optimize the opportunities within the medical landscape.
Introduction
Background
Science fiction has been viewed as an illusion that may not be easily achieved in these modern days, but metaverse and Artificial Intelligence have proved to be deal breakers in the future development of the healthcare industry. The reality of the science fiction that was perceived as impossible is getting into place through advancement in digitalization and automation in the healthcare industry, hence timely, better, and secure services. Metaverse is defined as a 3D environment that provides a virtual space that interconnects people globally to share social experiences, providing a chance for technology to leverage advancements, which may include Artificial Intelligence (AI), Virtual Reality (VR), And Augmented Reality (AR) (Mohamed, 2023). The technological advancements within the metaverse are essential in the medical field, such as seen in the focused future of AI-powered devices, which enables physicians to perform diabetic retinopathy screening while at home (Ahuja et al., 2023). Such advancements are essential in ensuring that patients in remote areas with challenging accessibility are provided with necessary treatments, hence, health equity (Bhugaonkar et al., 2022). Home-based treatment using AI power devices has adopted the use of chatbots, which are used with virtual reality and augmented reality to increase trust and acceptance among patients by making the medical service unemotional and less mechanized. Moreover, the healthcare industry has also benefited from the GenerateCT technology, which involves a framework that generates a 3D chest Computed Tomography (CT) using conditioning inputs that involve language text prompts to produce high-fidelity CTs. The technology has the ability to transform text into images or video, which has greatly transformed the healthcare industry, hence narrowing the gap in the visualization and diagnosis of diseases in patients. However, the generated CT is also still challenged by the ability to effectively and accurately analyze the images through manual and professional processes, which are time-consuming and prone to human bias. Medical image data analysis requires machine training through image recognition and classification to help in disease screening and diagnosis effectively. Electronic medical records (EMR) are also significant in medical image data analysis by providing medical history pertaining to a particular patient, including allergies, medication, and previous diagnostics imaging results (Huang et al., 2020).
Research Problem
Unique opportunities exist in the metaverse and AI integration, which include virtual comparative scanning and metaverse medical intervention, which hold a promising future in the medical field and human health. However, data leakage and network stability have proved to be vital challenges in the development and exploration of the opportunities presented by metaverse questioning security measures. However, security measures such as non-fungible tokens present a greater opportunity to be explored where distinct data units are registered in blockchains to provide a feasible solution to the problem of security and privacy concerns to the sensitive patients’ data that are used within the technology (Uribe & Waters, 2020). Moreover, Metaverse technologies create great security and privacy concerns, for it offers a virtual world where various activities such as shopping, commerce, playing games, education, fitness, attending shows, making friends, and various forms of social activities create the need to secure digital personal data (Canbay, 2022). One of the main concerns on privacy is contributed by the fact that most of the modern technological advancements are privately owned, which makes clinics and organizations have the need to play a significant role in the use and protection of medical data, hence data security concerns (Murdoch, 2021). The need to address security and privacy issues also raises ethical and regulatory concerns in the implementation of metaverse technologies in the healthcare system to ensure responsibility and transparency in the medical field. Digital identity and inclusivity in metaverse technology are great ethical concerns that require the establishment of clear guidelines for digital identity management and providing an inclusive environment regardless of users’ ability or background.
The opportunities presented by metaverse also need to undergo various development procedures to ensure that the advantages brought forth by the innovations benefit not only a minority group of wealthy persons but the entire population. However, metaverse also offers cost-effective medical research during clinical trials by supporting clinical trials, hence reducing the overall cost associated with the traditional procedure of trials, which involves the recruitment of patients, data collection, and control monitoring. Significant gaps in integrating metaverse technologies and AI in healthcare also involve the development of medical data analysis and medical imaging, which helps in diagnosis and therapy.
Research Objectives
One of the great research objectives is to develop a comprehensive understanding of Medical Technology and AI (MeTAI), especially in medical imaging, which is essential in enhancing diagnostic accuracy. MeTAI proves its importance in medical services when it can offer medical imaging data with a high level of precision because it involves matters of human lives that need to be accurate, hence avoiding loss of lives due to negligence or avoidable causes. MeTAI is also essential in medical training and education through a virtual environment for medical students who look into sharpening their skills, gaining hands-on experience, and staying up to date with the latest advancement in techniques such as text-to-image (Wang et al., 2023).
The research also seeks to Investigate the potential of metaverse approaches in healthcare through virtual comparative scanning and focusing on medical imaging presented by technologies such as Virtual Reality. VR offers a 3D world where users meet for interaction and exploration, hence offering the medical field the opportunity for diagnostic visualization in 3D, integration with existing imaging systems, and real-time data integration.
Moreover, the research is focused on enhancing medical image data generation through the application of GenerateCT to produce a computer tomography, which is one of the many medical image modalities that is widely adopted due to its effectiveness in cost and accessibility. Understanding and developing GenerateCT in medical image data generation is essential in ensuring realistic high-fidelity data generation, enhanced dataset training, and improved image generation, which ensures quality clinical applications such as diagnostics.
The research is also based on evaluating the performance and generalization of GenerateCT in medical image analysis tasks, which can be done through various procedures of testing, assessments, and continuous monitoring. `For instance, gathering feedback from patients and healthcare professionals and using the data in research to enhance the usability and effectiveness of GenerateCT.
Research Questions
The following research questions need to be addressed in the research and will be essential in guiding the research methodology and analysis of the findings to ensure that the discussion remains within the scope of the study.
Research question 1: How can the opportunities and challenges around the MeTAI metaverse be addressed for improved healthcare quality, accessibility, cost-effectiveness, and patient satisfaction?
Research question 2: What are the capabilities and limitations of GenerateCT in generating realistic and high-fidelity 3D chest CT volumes?
Research question 3: How does the integration of GenerateCT in medical image analysis tasks impact classification accuracy and generalization to external datasets?
Literature Review
The literature review section analyses the scope of the study by addressing two crucial aspects: intelligence healthcare advancement and text-conditional image generation.
Intelligence healthcare advancement
According to the research by Wang et al. (2022) on the development of metaverse in the context of healthcare, the technology has managed to blur the gap between physical and virtual realities not only in the entertainment industry but in the medical field. The research was set to identify the opportunities presented for the metaverse approach identified by a team of industrial, academic, regulatory, and clinical researchers in the healthcare sectors. The research states that the metaverse of MeTAI is essential in the facilitation of the development process of AI-based medical practices, prototyping, and regulation based on the required guidelines, adjustments, evaluation, and translation of the healthcare practices. The study represents the MeTAI ecosystem in the healthcare industry to involve four critical applications, which include virtual scanning, which is essential in establishing the most appropriate imaging technology; raw data sharing, which is essential in the tomographic data access in a controlled manner; augmented regulatory science which is essential in providing an elaborate scope of the virtual clinical trials; and metaverse medical intervention. For instance, the implementation of the four application ecosystem in a real case involves a patient being taken through a virtual CT scan as the initial procedure, which thereafter is a real scan, is conducted. The metaverse images are then made available for the healthcare team, which, upon agreement with the patient, gives the tomographic raw data of the patient and image to researchers for the purposes of innovations and improvements in technology. Augmented clinical trials are now made possible by integrating into the metaverse both the simulated images, data, and real images; the patients are then taken through metaverse-robotic surgery and rehabilitation.
Virtual comparative scanning has brought a remarkable advancement in clinical practice through integrating data from narrative to virtual objects that can be seen to create an understanding of a phenomenon or clinical concept. It is suggested that the text-to-image technology can be advanced through the building tomographic scanner that is vendor-specific and can modify illnesses in the avatars using an AI-enabled graphic tool. For instance, a virtual CT scanner type can be used by pathologies for a patient who has been referred for a CT scan in a heart problem diagnosis then using a deep reconstruction method, the images can be reconstructed and analyzed using AL tools. On the other hand, Raw data sharing is also a crucial concern in the development of medical tomographic scanners, for it is a necessary requirement in algorithmic optimization and can also be retained after tomographic image reconstruction. The challenge of accessing raw data has limited research practices pertaining to AI-based medical imaging, which has led to the rise of international society to democratize raw data and promote data-sharing open sites. Moreover, augmented regulatory science is also a concern in advanced medical imaging, where deep neural networks are prone to adversarial attacks, which are essential in optimizing safety. Metaversed medical interventions have enabled the ability to use virtual assistants’ technologies to conduct surgeries and various forms of therapies but still challenged with the need of a surgeon to work in an adjacent room. Metaverse medical interventions by use of patients’ data and use of medical tools to understand the underlying patient diseases and selection of the appropriate therapy will be the most profound impact of the metaverse and AI in the medical field. However, the research clearly discusses three major challenges that are experienced in the implementation of metaverse technology which include privacy and security concerns, management and investment, and disparity reduction (Wang et al., 2022).
Text-Conditional Image Generation
According to Hamamci et al. (2023), a study on generative modelling in the healthcare industry that involves the generation of images or videos from text has created a significant advantage in 3D chest CT scans. However, the technology is reported to have not exploited its full potential in generating CT images, hence the need to address the limitations. The study discusses the introduction of GenerateCT, which is a text-conditional CT generation method that can help bridge the gap that exists between the whole open-source framework and research in 3D medical imaging. GenerateCT is stated to entail three main components, which include a large language model which is retrained, a super-resolution diffusion model that is text conditional, and a 3D CT generation architecture transformer that is text conditional. The study also explains the need to produce 3DCT volumes that have a range of axial slices through the use of a proposed CT-ViT, which is said to effectively maintain auto-regressiveness in depth as it compresses the generated CT volumes. Therefore, the study confirms that GenerateCT is essential in the medical field to use medical language text prompts to generate 3D chest CT volumes that are of high fidelity, resolution, and realistic for the purpose of treatments. The research also investigates the use of the generated CT volumes to train a model to explore the potential of GenerateCT for chest CT volumes multi-abnormality classification, hence helping in the advancement of medical imaging research (Hamamci et al., 2023b).
Methodology
The recent developments in the deep –learning-technology and metaverse for the purpose of medical image generation has for many years presented various forms of opportunities for exp
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