OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

OBJECTIVE: To explore the efficacy of a Thalassemia risk assessment software for the screening of thalassemia mutation carriers and distribution of thalassemia genotypes detected by screening. METHODS: A total of 6 040 individuals were evaluated at Leshan Maternal and Child Health Care Hospital between 2022 and 2024 using the commonly used clinical thalassemia risk assessment method and the thalassemia screening software, respectively, and the performance indicators of the two methods were compared and analyzed against the result of thalassemia gene testing. This study was approved by the Ethics Committee of our hospital (Ethics No.: LfyLL[2022]005). RESULTS: The high-risk rate by the thalassemia screening software was 11.19%, with a sensitivity of 95.12%, specificity of 93.28%, positive predictive value of 43.20%, negative predictive value of 99.72%, and the area under the ROC curve (AUC) was 0.942. The thalassemia gene detection rate of the high-risk samples screened was 4.83%. The high-risk screening rate of the conventional method was 2.50%, with a sensitivity of 51.22%, specificity of 93.28%, positive predictive value of 80.79%, negative predictive value of 97.40%, and the AUC was 0.754. The thalassemia gene detection rate of the high-risk samples was 2.02%. CONCLUSION The software can effectively detect thalassemia carriers and significantly reduce the missed detection compared with conventional method, thereby significantly improve the efficacy of screening.
Humans ; Thalassemia/diagnosis* ; Software ; Female ; Genetic Testing/methods* ; Male ; Mutation ; Adult ; Genotype ; ROC Curve ; Risk Assessment

OBJECTIVES

The increasing prevalence of Kawasaki disease in Southeast Asia (SEA) and its potential relation with Coronavirus Disease 2019 (COVID-19) infection resulted in heightened interest in KD in the region, thus, this paper aimed to determine the trend and the socioeconomic facilitators of scientific productivity of KD research within the region. Specifically, this article determined the number of publication and citations related to KD per country, institution, and journal. We also explored the networks of countries within the region to the rest of the world and the keywords mostly associated with KD research in the region. Lastly, correlation of these bibliometric indices with socioeconomic factors in the region was analyzed.

METHODS

A literature search of KD papers in SEA was performed using Scopus database. We obtained bibliographic data from the available literature and visualized network of existing collaborations and keywords using VOSviewer software.

RESULTS

A total of 196 papers were included in the study. Bibliometric analysis showed a rising trend in publication within the region, most of which were from institutions in Singapore and Thailand. The most common topics on KD studies included clinical features, complications, treatment, and comorbidities. 

Country characteristics such as gross domestic product (GDP) per capita, research and development (R&D) expenditure (% GDP), and number of physician and R&D researchers were positively correlated with bibliometric indices of KD research in SEA. Moreover, number of international linkages was significantly associated with KD research productivity in the region. 

CONCLUSION

In summary, we showed an increasing trend of KD research in SEA. Funding allocation and capacity building are necessary to strengthen research productivity within the region.

Asia ; Asia, Southeastern ; Bibliometrics ; Capacity Building ; Coronavirus ; Covid-19 ; Database ; Disease ; Efficiency ; Gross Domestic Product ; Guanosine Diphosphate ; Infection ; Infections ; Literature ; Mucocutaneous Lymph Node Syndrome ; Paper ; Physicians ; Prevalence ; Publications ; Research ; Research Personnel ; Rest ; Singapore ; Socioeconomic Factors ; Software ; Thailand ; Therapeutics

BACKGROUND AND OBJECTIVES

Technological advancements are reshaping healthcare, particularly through mobile health (mHealth) applications that aid chronic disease management. Medication tracking apps, such as Simpill, have shown potential in improving outcomes for conditions like hypertension. However, disparities in digital literacy and concerns related to technology acceptance and privacy may hinder effective use. Grounded in the principles of the Design Thinking approach, this study sought to evaluate the relationship between health-seeking behavior (HSB), perceived barriers (PB), and the perceived usability (PU) of Simpill among middle-aged hypertensive adults. The research aimed to capture not only measurable associations but also to inform future app development through a user-centered lens that prioritizes empathy and real-world usability.

METHODS

A quantitative, descriptive-correlational research design was employed to assess respondents’ HSB, PB, and PU related to Simpill. The study was guided by core phases of the Design Thinking framework, particularly empathize and define, to ensure a deep understanding of user needs and usability constraints. Data were collected using a four-part, researcher-modified questionnaire administered to 138 purposively selected middle-aged adults (30–59 years old) residing in Barangay Industrial Valley, Zone 6, Marikina City, Philippines. All participants had a confirmed diagnosis of hypertension. Correlational analyses, including Kendall’s Tau B, were conducted to examine relationships among the variables. The integration of Design Thinking informed the development and interpretation of questionnaire items, aligning them with real-world challenges experienced by the target users.

RESULTS

The study investigated the relationship between HSB, PB, and the PU of Simpill among 138 middle-aged hypertensive individuals. Most respondents were female (55.8%), aged 50–59 (47.8%), and employed in non health-related sectors (95.7%). HSB levels were gene rally high (mean = 3.23), particularly in actively seeking health information, while lower engagement was noted in routine vital sign monitoring. PB were moderate (mean = 2.06), with unfamiliarity with the application cited as a common issue. PU was also rated as moderate (mean = 2.80), although ease of use received a low score (mean = 1.99). A weak positive correlation was found between HSB and PU (Kendall’s Tau B = 0.123, p = 0.049), while a moderate negative correlation existed between PB and PU (Tau B = -0.402, p < 0.001). These findings reflect insights derived from the Design Thinking "empathize" phase, suggesting that while proactive health behaviors may modestly support app engagement, unresolved user pain points—such as poor usability and lack of familiarity—remain significant obstacles to adoption. The results underscore the importance of moving to the "ideate" and "prototype" phases, where such user insights can directly shape the redesign and improvement of mHealth tools.

CONCLUSION

The study identified a high level of health- seeking behavior, reflecting the respondents’ engagement with their health and openness to guidance, consistent with the user-empathy foundation of Design Thinking. Moderate perceived barriers highlight existing challenges in technology adaptation, particularly among those who prefer traditional methods. The moderate PU rating of Simpill, especially in terms of ease of use, suggests the app’s current design does not fully align with user capabilities or expectations. In line with Design Thinking principles, particularly user-centered innovation, the findings emphasize the need to involve users in iterative co-design processes to improve mHealth solutions. Addressing perceived barriers through enhanced digital literacy, usability testing, and interface refinement could substantially boost app acceptance and effectiveness in real-world settings.

Human ; Hypertension ; Mobile Applications ; Health Behavior ; User-centered Design

OBJECTIVES

This study aims to create a study protocol that will explore UST administrators’ perceptions of the benefits and risks of AI use in higher education learning environments.

METHODS

A qualitative descriptive design will be employed, using semi-structured interviews with at least fifteen administrators selected through purposive sampling. Audio-recorded interviews will be transcribed verbatim and subjected to thematic analysis using NVivo software

RESULTS

Administrators from different college-level fields perceive and engage with AI across various academic contexts. Exploring these perceptions will allow guidance in the development of coherent, contextually grounded institutional policies that promote responsible GenAI use and support digital leadership in Philippine higher education.

Human ; Artificial Intelligence ; Universities ; Software ; Administrative Personnel ; Intelligence ; Risk ; Policy

Introduction: Current education on diabetes self-management is time-consuming as it is conducted face-to-face. This study aimed to evaluate the effectiveness of diabetes education via mobile application on hemoglobin A1c (HbA1c). Methodology: This was a double-blinded randomized controlled study conducted in the Department of Endocrinology, Hospital Pulau Pinang and Hospital Raja Permaisuri Bainun. Participants were stratified randomized according to HbA1c to intervention group (using T-Manis) and control group. The primary objective was to assess the difference in HbA1c between the groups. Secondary objectives were to assess the difference in body weight, hospitalization related to diabetes, diabetes knowledge between groups and usability and satisfaction of T-Manis. Laboratory investigations and questionnaires were conducted at baseline, 4 months and 8 months. Results: A total of 112 participants were randomized into two groups each with 56 participants. Mean age was 51 years with similar diabetes duration of 16 years in both groups. Participants comprised of 61.6% females with ethnic distribution of 49.1% Malay, 30.4% Chinese, 19.6% Indian and 0.9% Siamese. Baseline HbA1c levels were comparable between intervention and control groups (10.04% versus 9.91%; mean difference 0.13%; 95% CI −0.56 to 0.81; p = 0.718). At 4 months, HbA1c of the intervention group demonstrated a significant reduction compared to the control group (8.93% versus 9.80%; mean difference −0.86; 95% CI −1.59 to −0.14; p = 0.02). Sustained reduction of HbA1c in the intervention group was observed at 8 months (mean difference −1.01; 95% CI −1.85 to −0.16; p = 0.02). Baseline body weight was 77 kg, with no betweengroup differences in body weight, hospitalization related to diabetes and diabetes knowledge scores, although diabetes knowledge scores improved at 4 months in the intervention group. 89.3% of participants reported satisfaction with T-Manis Conclusion The T-Manis mobile application aided in significantly improving glycemic control with high user satisfaction, supporting its feasibility as an adjunct to standard diabetes care.
Diabetes Mellitus, Type 2 ; Glycemic Control ; Mobile Applications ; Pangolins

INTRODUCTION: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc., Seoul, Republic of Korea) segmentation software in a Southeast Asian community-based cohort with normal cognition, mild cognitive impairment (MCI) and dementia. METHOD: Study participants belonged to the community-based Biomarker and Cognition Study in Singapore. Participants aged between 30 and 95 years, having cognitive concerns, with no diagnosis of major psychiatric, neurological or systemic disorders who were recruited consecutively between April 2022 and July 2023 were included. Participants underwent neuropsychological assessments and structural MRI, and were classified as cognitively normal, with MCI or with dementia. MRI pre-processing using automated pipelines, along with human-based visual ratings, were compared against AI-based automated AQUA output. Default mode network grey matter (GM) volumes were compared between cognitively normal, MCI and dementia groups. RESULTS: A total of 90 participants (mean age at visit was 63.32±10.96 years) were included in the study (30 cognitively normal, 40 MCI and 20 dementia). Non-parametric Spearman correlation analysis indicated that AQUA-based and human-based visual ratings were correlated with total (ρ=0.66; P<0.0001), periventricular (ρ=0.50; P<0.0001) and deep (ρ=0.57; P<0.0001) white matter hyperintensities (WMH). Additionally, volumetric WMH obtained from AQUA and automated pipelines was also strongly correlated (ρ=0.84; P<0.0001) and these correlations remained after controlling for age at visit, sex and diagnosis. Linear regression analyses illustrated significantly different AQUA-derived default mode network GM volumes between cognitively normal, MCI and dementia groups. Dementia participants had significant atrophy in the posterior cingulate cortex compared to cognitively normal participants (P=0.021; 95% confidence interval [CI] -1.25 to -0.08) and in the hippocampus compared to cognitively normal (P=0.0049; 95% CI -1.05 to -0.16) and MCI participants (P=0.0036; 95% CI -1.02 to -0.17). CONCLUSION Our findings demonstrate high concordance between human-based visual ratings and AQUA-based ratings of WMH. Additionally, the AQUA GM segmentation pipeline showed good differentiation in key regions between cognitively normal, MCI and dementia participants. Based on these findings, the automated AQUA software could aid clinicians in examining MRI scans of patients with cognitive impairment.
Humans ; Cognitive Dysfunction/pathology* ; Magnetic Resonance Imaging/methods* ; Male ; Middle Aged ; Female ; Aged ; Artificial Intelligence ; Software ; Dementia/diagnostic imaging* ; Aged, 80 and over ; Adult ; Singapore ; Neuropsychological Tests ; Brain/pathology* ; Cohort Studies ; Gray Matter/pathology* ; Southeast Asian People

Process analytical technology(PAT) is a key means for digital transformation and upgrading of the traditional Chinese medicine(TCM) manufacturing process, serving as an important guarantee for consistent and controllable TCM product quality. Near-infrared(NIR) spectroscopy has become the core technology for measuring the TCM manufacturing process. By incorporating NIR spectroscopy into PAT and starting from the construction of a smart platform for the TCM manufacturing process, this paper systematically described the development history and innovative application of the combination of NIR spectroscopy with chemometrics in measuring the TCM manufacturing process by the research team over the past two decades. Additionally, it explored the application of a validation method based on accuracy profile(AP) in the practice of NIR spectroscopy. Furthermore, the software development progress driven by NIR spectroscopy supported by modeling technology was analyzed, and the prospect of integrating NIR spectroscopy in smart factory control platforms was exemplified with the construction practices of related platforms. By integrating with the smart platform, NIR spectroscopy could improve production efficiency and guarantee product quality. Finally, the prospect of the smart platform application in measuring the TCM manufacturing process was projected. It is believed that the software development for NIR spectroscopy and the smart manufacturing platform will provide strong technical support for TCM digitalization and industrialization.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/analysis* ; Software ; Medicine, Chinese Traditional ; Quality Control

This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male

Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users' intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.
Brain-Computer Interfaces/ethics* ; Artificial Intelligence/ethics* ; Humans ; Deep Learning ; User-Computer Interface ; Electroencephalography