Orthodontic treatment is a common dental treatment. A complete treatment often takes several years and is costly. In order to increase the degree of satisfaction and reduce the gap between the expectations of the patient and the limitations of orthodontics, orthodontists and patients should have sufficient communication. A simulation system can provide a good channel for communication between orthodontists and patients. This paper proposes a 3D dentist–patient communication system for the simulation of orthodontics in advance. The system collects the clinical paradigms of orthodontic cases, which must contain photos from before, during, and after maxillofacial treatment. This research simulates treatment processes by tuning a 3D virtual model of the oral and maxillofacial areas, including the face, mouth, and teeth, to demonstrate the processes of clinical paradigms. These 3D models could be edited and synthesized to generate new 3D models for simulation with the 3DS Max tool. In addition, the proposed system provides a function for the orthodontist to manually operate the 3D virtual model, such as tooth arrangement, morphing of the gums with movement of the teeth, the placement (attachment) of arch-wires and brackets, and changes of facial appearance. The orthodontist can demonstrate the treatment processes, show movements of the teeth, and answer possible questions from the patient about the treatment processes by using the 3D model. To show the effectiveness of the proposed system, a questionnaire about the system was also used to demonstrate its acceptance, usability, and validity. Qualitative interviews with dentists and questionnaires for patients about the system are both performed. The results showed that the proposed system is an effective vehicle for communication between patients and orthodontists.

As complex black-box Artificial Intelligence (AI) models become integral to high-stakes domains like fraud detection, the need for transparency is becoming critical. Explainable AI (XAI) aims to make algorithmic decisions understandable to stakeholders. While systematic reviews have mapped the use of XAI in the broader financial sector, a focused synthesis of the methodological challenges unique to fraud detection remains a critical gap. This systematic literature review addresses this gap, following PRISMA 2020 guidance and synthesizing 49 peer-reviewed studies (2021–2025) to identify current practices and foundational challenges. Our analysis reveals that while post-hoc methods like SHAP and LIME are prevalent, their application is undermined by two systemic methodological flaws that threaten the validity of current research. First, we identify an explainability–imbalance paradox, where common data resampling techniques used to manage class imbalance can distort background distributions or local neighborhoods and thereby compromise the faithfulness and fidelity of post-hoc explanations. Second, we discover a profound evaluation vacuum, with around 80% of the analyzed studies using model predictive performance as a proxy for explanation quality rather than directly evaluating the explanations themselves. We also critically assess when post-hoc explanations are appropriate versus when intrinsically interpretable models are preferable in auditable and high-risk contexts. Based on these findings, we propose an explanation-aware training and evaluation checklist and a research agenda to guide the field toward standardized, explanation-level evaluation and the development of imbalance-robust, explanation-preserving data-processing methods.

Currently, the Taiwan Electronic Medical Record Exchange Center uses the Clinical Document Architecture (CDA) framework, which is based on the international medical standard. The CDA R2 standard, defined in 2005, is used for cross-institution retrieval of electronic medical records (Ministry of Health and Welfare, Information Department, 2021). However, CDA R2 only supports the exchange of clinical documents and is limited to the XML format. Due to the lack of a standardized framework for medical data exchange in Taiwan, different standards and specifications result in different data interface methods between systems, requiring customization for each system by healthcare institutions or the government. The inconsistency in data formats requires healthcare institutions and the government to spend more time on data parsing and mapping, resulting in slow integration of medical data. In this study, we simulated healthcare institutions using Fast Healthcare Interoperability Resources (FHIR) for medical information exchange and utilized the exchanged medical information to create a dynamic dashboard to assist healthcare professionals in making medical decisions. To ensure information security, we employed Hyper Text Transfer Protocol Secure (HTTPS) for secure transmission, which encrypts the transmitted medical record data using the Transport Layer Security (TLS) protocol, preventing deliberate interception and tampering of medical record data between the two systems. Finally, to test the load and performance of static and dynamic resources and web applications, we conducted a system performance evaluation using Apache JMeter. The results of this study demonstrate that replacing the gateway of the Electronic Medical Record Exchange Center with an FHIR server effectively reduces the time and cost spent by developers on data format conversion while also mitigating the information security risks associated with the previous VPN solution. Additionally, by utilizing dynamic charts, healthcare professionals are assisted in making medical decisions.