BackgroundSmart home technologies and assistive robots play a role in enhancing the well-being of older adults. This study aims to evaluate the factors and acceptance level of these technologies among seniors and to explore the factors affecting their acceptance.MethodsThree databases were systematically searched using keywords to identify relevant articles. The retrieved studies were screened based on eligibility criteria. Key features of the studies and the acceptance status of the aforementioned technologies among seniors were documented in a data extraction form.ResultsTwenty-seven studies met the eligibility criteria. Robots (74.1%), sensors (18.5%), wireless technologies (3.7%), and smart home voice assistants (3.7%) were utilized by seniors. Remote patient monitoring (33.3%) was the most prevalent application of these technologies. Approximately 89% of the studies reported positive attitudes toward these technologies. "Technology convenience" (22.2%) emerged as the most significant reason for smart technology acceptance. Conversely, "concerns about privacy and security" (14.8%) and "lack of need for technology" (14.8%) were the most frequently cited reasons for non-acceptance.ConclusionsTo enhance the adoption of reviewed technologies, it is crucial to implement strategies that raise awareness, ensure data security, and address the actual needs of this demographic in both the design and implementation phases.

ObjectiveTo conduct a multidimensional evaluation of endometrial cancer (EC)-related videos on major Chinese short-video platforms.MethodsA cross-sectional study conducted on May 8, 2025 analyzed 226 eligible EC-related videos from TikTok, Rednote, and Bilibili. Video quality was assessed using Global Quality Scale (GQS) and Video Information and Quality Index (VIQI), reliability using modified DISCERN (mDISCERN), and understandability and actionability using Patient Education Materials Assessment Tool (PEMAT).ResultsAmong 226 videos, TikTok emphasized symptoms/risk factors and scored highest in engagement, reliability (mDISCERN=2.0, P=0.002), and understandability (88%). Bilibili led in VIQI (median=17.0) but had the lowest understandability (67%). Professional videos outperformed patient-generated content (all P<0.05). Video length correlated positively with quality but negatively with engagement and understandability (all P<0.01).ConclusionsEC-related videos vary widely in quality and lack consistent actionability. Strategic content design and platform-level verification are needed to improve reliability and public health impact.

Clinical trials are vital for advancing care. However, a systematic approach to tracking trial participation across different facilities and sponsors has been lacking. We developed natural language processing (NLP) methods to extract study enrollment history, including enrollment status, consent date, and study title from information on clinical trial participation recorded in clinical notes in the electronic health record based on national Veterans Affairs electronic health record data. The method exhibited high test-set precision for enrollment status (0.94), consent date (0.97), and study title (0.87) and acceptably high recall (0.76, 0.70, and 0.84, respectively). From a single center, the classifier correctly identified 111 of 125 trial participants (88.8%) across 12 distinct trials. Our study demonstrates the feasibility of using NLP to capture trial enrollment from a nationwide healthcare system. This algorithm creates a novel data resource for analyzing and tracking trial enrollment at the population level.

BackgroundLarge language models (LLMs) have demonstrated promising capabilities in medical diagnostic reasoning, yet their performance in specialized clinical domains such as rheumatology remains incompletely characterized. While diagnostic accuracy has been evaluated, critical dimensions including calibration, reasoning quality, and temporal stability have not been systematically assessed across contemporary models.ObjectivesThis study aimed to comprehensively evaluate and compare the diagnostic accuracy, certainty expression, reasoning quality, and hallucination rates of four state-of-the-art LLMs ChatGPT-4, Claude 3.5, DeepSeek-V3, and Gemini 1.5 Pro in complex rheumatologic case scenarios.DesignA cross-sectional, analytical, and comparative study was conducted following STARD and TRIPOD guidelines, adapted for LLM evaluation. Nine complex rheumatologic cases from published case reports were evaluated at three time points (Days 1, 5, and 10) between July 1 and September 18,2025.MethodsStandardized clinical vignettes were submitted to each LLM under controlled experimental conditions. Two blinded senior rheumatologists independently assessed diagnostic accuracy, reasoning quality across five analytical dimensions using Likert scales, and hallucination frequency. Certainty expression and temporal stability were quantified using intraclass correlation coefficients. Correlation analyses examined relationships between reasoning quality and confidence expression.ResultsAll models achieved near-perfect diagnostic accuracy, with ChatGPT, Claude and Gemini correctly identifying the primary diagnosis in 100% of cases and DeepSeek in 88.9%. However, Spearman correlation analysis revealed uniformly weak and non-significant associations between reasoning quality and expressed certainty across all models (ρ range: -0.156 to 0.215, all p>0.05), indicating fundamental miscalibration. ChatGPT demonstrated the highest reasoning score (3.89±0.23) and lowest hallucination rate (7.4%), while Gemini showed the highest hallucination frequency (18.5%). Temporal stability was excellent for ChatGPT (ICC=0.84) and good for DeepSeek (ICC=0.79).ConclusionDespite exceptional diagnostic accuracy, current LLMs exhibit critical limitations in confidence calibration and variable hallucination rates, representing significant barriers to safe clinical deployment in rheumatology.

Objective: This study presents a structured evaluation of large language models (LLMs) in predicting suicide methods based exclusively on indirect forensic psychiatric indicators. Methods: Ninety-two forensic psychiatric cases (2019-2024), involving survivors of suicide attempts formally examined in medico-legal contexts, were retrospectively analyzed. Variables included age, sex, psychiatric diagnosis, previous suicide attempts, psychiatric medication use, impulsivity, and consciousness at emergency admission. Six LLMs were tested: ChatGPT-4o, ChatGPT-4o Mini, ChatGPT-O3 (OpenAI), Gemini 2.0 Flash, Gemini 2.5 Pro, and Gemini 2.5 Flash (Google DeepMind). Each case was converted into a standardized anonymized prompt. Model predictions were categorized by blinded forensic physicians and evaluated using accuracy, precision, recall, F1-score, and Cohen's Kappa for 1-month reproducibility. Results: Gemini 2.5 Flash achieved the highest performance with 76.09% accuracy, 46.9% F1-score, and 45.2% recall. It accurately predicted the dominant method, medication overdose, but underperformed for rare categories. Temporal reproducibility was moderate (κ = 0.582), while other models exhibited lower and less stable performance. Conclusion: LLMs can infer suicide methods from indirect psychiatric data with encouraging accuracy. However, limitations in detecting rare methods and maintaining temporal consistency suggest the need for further methodological refinement and external validation prior to forensic application.

BackgroundData quality encompasses completeness, accuracy, integrity, timeliness, and confidentiality. In many low- and middle-income countries, including Somalia, data from RHIS are often poor, limiting their usefulness for public health actions. This study assessed the quality of RHIS data and associated factors among public health facilities in the Banadir region, Somalia.MethodsA facility-based cross-sectional study was conducted from October to December 2024 across 36 public health facilities using a multistage sampling approach. Data were collected through document reviews, interviews, and observations using PRISM-based standardized tools. Data were analyzed in SPSS version 27 after checking logistic regression assumptions. Data quality was assessed by the dimensions of accuracy (≥80%), completeness (≥85%), and timeliness (≥85%). Bivariable and multivariable logistic regression analyses identified associated factors.ResultsA total of 398 healthcare workers (59.5% female) participated, yielding a 98% response rate. Overall, good-quality data were observed in 65.3% of departments. Departments in health centers were 2.7 times more likely to report good-quality data than hospitals. Feedback, refresher training, and user-friendly reporting formats were significantly associated with better data quality.ConclusionData quality across the three dimensions was scored at (65.3%). Strengthening supervision, feedback, and context-specific training can improve data reporting and management.

Big Data in Internet of Healthcare Things (IoHT) environments includes large volumes of structured and unstructured clinical information. The Hadoop Distributed File System (HDFS) is widely used for its scalability and ability to run on commodity hardware. However, it offers limited native encryption, leaving data vulnerable to security risks. Although several encryption techniques exist, traditional algorithms still face performance and security limitations with large-scale medical datasets. Therefore, this study introduces a hybrid encryption framework designed to enhance security in IoHT environments that process large-scale medical Big Data. The framework combines Attribute-Based Encryption (ABE) with the Blowfish cipher to secure data generated by heterogeneous medical devices across the IoHT infrastructure. The proposed approach is benchmarked against established hybrid schemes-CP-ABE + HE, HE + BF, and CP-ABE + AES-to provide a comparative assessment of its security strength and computational performance. The performance assessment employed key computational metrics, including system efficiency, encryption latency, and decryption latency. Experimental results demonstrate that the proposed hybrid scheme delivers superior performance compared to existing approaches, attaining a peak efficiency of 98.5%. The method further achieved encryption and decryption times of 6.8min and 5.7min, respectively, indicating improved computational handling of large-scale IoHT data.

Objectives: During the early phase of the COVID-19 outbreak, misinformation spread rapidly, hindering effective health communication and fueling xenophobic violence. The politicization of health issues, along with the manipulation by social bots and astroturfing accounts, posed significant challenges. This study aims to investigate how misinformation spreads through social media, involving malicious actors like trolls and bots, and explores emotional contagion during public health crises. Methods: Using a computational methodology that combines semantic modeling, social network analysis, bot identification, emotion analysis, and time series analysis, the study analyzed over 700,000 tweets from February to July 2020. Results: The findings reveal that inauthentic actors amplified negative emotions, particularly among news and political actors, while positive emotions were less prominent. Astroturfing accounts acted as key nodes, perpetuating negative emotional contagion. Conclusion: This study provides a framework for monitoring emotional responses in public health crises, with findings applicable beyond COVID-19 to other public health emergencies.

Objective. This cross-sectional study compared the color accuracy and image quality of intraoral photographs taken with DSLR cameras, smartphones, and smartphones with auxiliary lighting. Methods. Forty participants had five images captured per device, yielding 600 images. Image quality was evaluated using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) via No-Reference Matrix (NRM), while experts provided qualitative evaluations. Results. Smartphones achieved the highest average NRM score (53.14 ± 28.2), followed by smartphones with auxiliary lighting (47.62 ± 1.9) and DSLR cameras (45.32 ± 2.2), with no significant difference between DSLR cameras and smartphones with auxiliary lighting (p = 0.34). Color accuracy (ΔE) was closest between DSLR cameras and smartphones with auxiliary lighting (4.89 ± 2.47), while other pairs showed higher differences. These two also showed comparable color consistency (p = 0.63 and 0.57 for a; b values), although smartphones produced brighter images (L = 50.56 ± 4.82 vs. 31.84 ± 4.82). Experts preferred DSLR images for caries diagnosis and presentation (96.7% preference), but found smartphone images with auxiliary lighting clinically acceptable. Conclusion. While DSLR cameras delivered superior image quality, smartphones with auxiliary lighting demonstrated comparable diagnostic performance as a practical, low-cost alternative in resource-limited settings. Further validation with newer devices is recommended.

Background: Periodontal disease (PD) is a primary contributor to tooth loss, which negatively affects oral functionality and quality of life. This research aims to investigate the effectiveness of various machine learning (ML) classifiers in identifying PD among U.S. adults. Method: Nineteen features, selected based on prior literature and expert dentist input, were preprocessed using feature engineering techniques. Eleven machine learning classifiers, including basic and ensemble models, were evaluated to identify the best performing model. The interpretability of the model was evaluated using Shapley additive explanations and individual conditional expectation plots to determine key predictors of periodontitis. Results: The predictive efficacy of the ML classifiers is assessed using metrics such as the area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. The CatBoost classifier performed best in identifying PD. It achieved an AUC of 84.5%, an accuracy of 75.8%, a precision of 75.8%, a sensitivity of 78.8%, and a specificity of 72.5%. Having an annual dentist visit and age emerged as the most influential variables. Conclusions: The ML models utilized in this study exhibited robust predictive performance and can be further improved by incorporating additional clinical parameters. The proposed models effectively identified individuals at high risk for developing PD.