Journal of Medical Systems最新文献

The increasing adoption of digital education, including mobile learning (mLearning), is transforming the training of future health professionals, offering advantages such as improved accessibility, timeliness, and affordability. While mLearning enhances clinical training by providing flexible access to information and supporting practical skills development, challenges such as inadequate resources and resistance from staff and patients need to be addressed for successful integration. This review aims to explore the factors for successful adoption of mLearning in clinical placements, providing valuable insights to inform best practices in its implementation. A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR). Arksey and O'Malley and Joanna Briggs Institute (JBI) methodology was applied. Searching 6 electronic databases plus manual searching returned 5178 articles, with 76 articles included in analysis. All studies were published between 2008 and 2024, with a significant proportion originating from Canada (n = 12), the United States (n = 12), the United Kingdom (n = 11), and Australia (n = 11). The majority of the studies involved nursing (n = 47) and medical (n = 24) students. The analysis revealed 6 facilitators and 6 barriers. It identified positive attitudes toward mobile applications, highlighting their role in enhancing education in clinical environment, organizing training, and improving patient care. However, barriers such as the absence of regulations, ethical concerns, and technical issues were also noted, along with the need to address information literacy and skills. Mapping research regarding facilitators and barriers for introducing mobile learning technologies into undergraduate education in clinical environment has helped in creating a set of solutions which are capable to ensure the success and sustainability of mLearning. These solutions should be considered at the innovation's design, implementation, and post-implementation stages to guarantee its effectiveness in education in clinical environments. This may help to enhance the learning experience, improve knowledge retention, and develop clinical skills, while providing a cost-effective solution for clinical training programs for healthcare professions. This, in turn, has positive implications for quality of care provided.

The purpose of this scoping review is to explore the current state of digital scribe technology in primary care, focusing on how automatic speech recognition (ASR) and natural language processing (NLP), which are foundational technologies behind artificial intelligence (AI) systems used in digital scribes contribute to their effectiveness, integration, and adoption. The Joanna Briggs Institute (JBI) guidelines for scoping reviews was utilized alongside reporting according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Searches through PubMed, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature yielded 29 relevant studies from 14,866 studies, spanning six countries and from 2018 to 2024. Digital scribes demonstrated effectiveness in reducing documentation time, which directly enhances workflow efficiency and allows clinicians to spend more time interacting with patients. Digital scribes, while promising in improving clinical documentation, face significant integration challenges and adoption barriers, particularly in adapting to diverse healthcare workflows. The findings of this scoping review reveal several implications for the existing literature on digital scribes, particularly regarding the need for comprehensive studies on effectiveness in real-world primary care settings. This study highlights the promising role of digital scribes in primary care, where ASR and NLP technologies have demonstrated the potential to enhance documentation accuracy, streamline workflows, and reduce clinician burden.

Assessing usability and satisfaction is vital to ensure the efficiency and optimal use of mobile health (mHealth) applications. Nevertheless, existing questionnaires revolve around computerized systems and lack validation for evaluating mHealth applications. We aimed to develop and validate a tri-language questionnaire to assess usability and satisfaction of mobile health applications (USHA). This study consisted of three phases: item development, translation, and validation. During the item development phase, a preliminary English version of the USHA questionnaire that comprised Likert-scale and demographic items was designed. Subsequently, forward-backward translation was performed to produce Malay and Chinese versions. Content validation was conducted with eight experts, followed by face validation with five diabetes mellitus patients. Reliability testing was conducted through test-retest analysis among diabetes mellitus patients. The initial tri-language USHA questionnaire consisted of 18 Likert-scale items and 8 demographic items. Following expert validation, five Likert-scale items and one demographic item were eliminated for lack of relevance, importance, or clarity, while four Likert-scale items were rephrased. During face validation, additional one demographic item was removed. The finalized questionnaire demonstrated high reliability, with a Cronbach's alpha of 0.956 and an intraclass correlation coefficient of 0.845. Consequently, the tri-language USHA questionnaire consisted of 13 Likert-scale items and six demographic items, is a valid and reliable instrument that enhances accessibility and enables assessment of the usability and satisfaction of interactive mHealth applications, especially for diabetes mellitus care across a broad range of users.

This study systematically investigates the alterations in electroencephalogram (EEG) microstates in patients with refractory epilepsy(RE) across different seizure stages. A novel EEG microstate analysis framework is proposed to address the limitations of traditional methods in clinical diagnosis and treatment. Additionally, the study explores the feasibility of utilizing microstate characteristics for seizure recognition and classification. Two independent datasets were used to extract microstate features corresponding to the four canonical seizure stages. A directed microstate graph structure was constructed, and a directed graph convolutional network(DGCN) was employed for classification. The performance of the proposed framework was compared to that of traditional methods, which rely on manually extracted features and classical machine learning classifiers. The proposed framework (termed MsG-GCN for reference within this article) exhibited superior classification performance, achieving an accuracy of 80.2%, compared to the best traditional method (Support Vector Machine, SVM), which achieved 74.3%. Notably, microstates A and C showed significant differences across seizure stages, with the average occurrence rate exhibiting greater discriminative power than the average duration and coverage. This study introduces novel approaches for the automated classification of epileptic seizures, demonstrating the effectiveness of graph neural networks in modeling dynamic epileptic microstate transitions. The proposed framework not only enhances classification performance but also provides a highly interpretable paradigm for intelligent, auxiliary diagnosis of complex neurological disorders.

This narrative review evaluates the current status, potential value, key challenges, and future directions of Microsoft HoloLens 2 mixed reality (MR) technology, with a particular focus on its built-in eye tracking and visual perception functions, in the context of pediatric orthopedic three-dimensional model teaching. Relevant literature on medical education and surgical training was integrated to examine the technical features, teaching practices, and educational implications of HoloLens MR. Existing studies indicate that MR technology can enhance learners' spatial understanding and operative skills; eye tracking supports the quantification of learning processes and personalized feedback, while visual perception technologies improve immersion and interactivity. However, limitations remain regarding hardware performance, content development costs, quality of research evidence, privacy concerns, and ecological sustainability. The application of HoloLens MR in pediatric orthopedic education holds broad prospects. Its sustainable integration into medical education will depend on advances in hardware, integration of artificial intelligence, expansion of remote collaboration, and the establishment of standardized evaluation systems.

Clinical documentation demands necessitate automated solutions balancing clinical precision with patient comprehension. This study aims to develop and validate a unified framework that maintains diagnostic accuracy while dynamically adapting medical report complexity to diverse literacy levels, and to establish comprehensive evaluation methodologies for patient-centered medical documentation. We developed a unified framework integrating three innovations: a hybrid detection method combining CheXFusion and Eigen-CAM for clinical finding detection and anatomical localization; an advanced LLaVA-based pipeline synthesizing clinical predictions with anatomical data for contextually rich medical reports; and a self-reflective large language model system dynamically adapting report complexity across reading levels (6th, 11th, and 18th-grade) while preserving clinical integrity. Our methodology introduces novel evaluation using the Mistral-small model assessing report quality through consistency, coverage, and fluency metrics. Validation on MIMIC-CXR and IU X-Ray datasets demonstrated substantial improvements: 19.78% enhancement in classification accuracy (AUROC), 17.29% improvement in mean average precision, 56.88% increase in patient comprehension scores, and 5.26% gain in diagnostic precision. The framework successfully addresses maintaining clinical rigor while enhancing patient accessibility, reducing documentation burden on healthcare providers and improving patient engagement through comprehensible reporting. This work establishes new standards for automated medical documentation that effectively reconcile clinical precision with patient comprehension in healthcare communication.

Medical misinformation is a major public health concern. The public increasingly uses artificial intelligence (AI) tools for medical consultations. Therefore, concerns arise about their ability to detect and even correct subtle medical information that users may be embedding in users prompts. This study assessed the ability of different ChatGPT models in detecting and correcting such subtle misinformation. Fifty clinical plausible prompts with subtle medical misinformation were introduced separately to ChatGPT models 4o, 4.1-mini, and GPT-5. Prompts spanned Internal Medicine, Cardiology, Pediatrics, Ophthalmology, and Oncology. Responses were scored on a 3-point scale: 0: No correction; 1: Hedging or uncertainty; 3: cutting edge detection and correction. GPT-4o was the best performing model, surpassing GPT-5 by correctly identifying and correcting misinformation in 86% of the prompts compared to 74% for GPT-5. GPT-4.1-mini showed weaker performance, detecting dsmisinformation in only 52% of prompts, with complete failure in 34% and hedging in 14%. Specialty-specific analysis revealed that GPT-4o achieved higher detection rate in all tested specialties compared to GPT-4.1-mini and GPT-5. Only oncology showed comparable detection rates between GPT-4o and GPT-5. Although the performance of GPT-4o and GPT-5 in detecting subtle medical misinformation was promising, unexpectedly, GPT-4o surpassed GPT-5 in performance. Using underpowered variants such as GPT-4.1-mini, poses a public health threat. Reverse prompting offers a diagnostic lens and should be integrated into standard AI safety testing protocols.

A health equity movement is underway, in which broad sectors of society must work together to create solutions to the complex interwoven problems that undermine equal opportunities for good health and well-being. Yet, addressing health inequity is a complex and challenging problem. Health inequity manifests through complex disparities that overload healthcare services and penetrate (all) other sectors of society. The aim of this study is to reach consensus on health equity related topics to be included in European health and social care study programmes by using the Sustainable Development Goals (SDGs). To identify such topics, a Delphi method was designed and performed in an expert panel comprising nine academics, clinicians, and members of a non-governmental organization. Using the Sustainable Development Goals as a framework, three rounds of surveys were conducted. The response rate was 100% across all rounds. In the first round, participants selected relevant SDG targets and indicators; 183 indicators were shortlisted. In the second round, participants rated the relevance of each indicator, leading to the endorsement of 142 indicators. In the third round, 162 out of 247 total indicators were endorsed. None of the Sustainable Development Goals were considered irrelevant to health and social care study programmes. We argue that to address health inequities effectively, health and social care professionals should liaise with a wide range of stakeholders in non-health sectors to design appropriate strategies to improve health and well-being. This implies that health and social care curricula should integrate competencies and capabilities that allow future professionals to work outside their traditional spheres of practice, communicating health information to a broad range of audiences, advocating and translating data for intersectoral action, and negotiating strategies and approaches to attain health equity in collaboration with stakeholders from different social sectors.

Technological advancements are enhancing healthcare, with digital twin (DT) technology emerging as a key tool for personalized and efficient care. This umbrella review systematically evaluates the literature on DT applications in healthcare, focusing on their effectiveness, challenges, and potential to substantially improve patient care.An umbrella review was conducted following the Joanna Briggs Institute (JBI) manual for evidence synthesis and the PRISMA guidelines. A comprehensive literature search was performed across multiple databases, including PubMed, Scopus, Web of Science, and IEEE Xplore, targeting systematic reviews published up to July 2024. The inclusion criteria focused on systematic reviews and meta-analyses related to the usage of DT technologies in healthcare settings.The review identified a considerable number of systematic reviews that highlight the role of DTs in various domains of healthcare, including personalized medicine, predictive maintenance of medical equipment, and healthcare system optimization. Key themes included the integration of real-time data and predictive modeling, which enhance chronic disease management and surgical planning. However, barriers to implementation were noted, including data privacy concerns, validation issues, and high costs.DT technology has the potential to enhance healthcare delivery by enabling personalized treatment and improving operational efficiencies. However, addressing ethical challenges, particularly concerning data privacy and security, is crucial for the successful integration of DTs in clinical practice. This umbrella review underscores the need for continued research to overcome these challenges and facilitate the widespread adoption of DT technologies in healthcare.