Journal of Medical Systems最新文献

Large language models (LLMs) are increasingly used for medical advice; despite this, their response readability and quality remain suboptimal. Current research focuses on evaluating LLM outputs, with little investigation into practical optimization strategies for clinical use. On August 9, 2025, we identified the top 25 search keywords for five common cancers via Google Trends and adapted them into six prompt types. Each was posed to ChatGPT-4o and ChatGPT-5 between August 10 and August 12, 2025 under two query conditions: isolated (single question per page) and aggregated (all questions for one cancer type on the same page). Readability was assessed using four indices: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FKRE), Gunning Fog Index (GFI), and the Simple Measure of Gobbledygook (SMOG). Quality was evaluated on a 5-point Likert scale across accuracy, relevance, comprehensiveness, empathy, and falsehood. ChatGPT-5 generated responses with significantly fewer words (316.81 ± 12.96), sentences (19.79 ± 1.01), syllables (551.93 ± 24.55), and hard words (62.33 ± 3.60) than ChatGPT-4o (292.85 ± 14.52, p = 0.003; 18.77 ± 1.07, p = 0.039; 515.01 ± 27.89, p = 0.006; 58.35 ± 4.05, p = 0.005), while also achieving higher scores in accuracy (W = 2.116, p = 0.034), relevance (W = 2.454, p = 0.014), comprehensiveness (W = 2.574, p = 0.010), and empathy (W = 2.174, p = 0.030). The 6th-grade prompt markedly improved readability over other strategies (ChatGPT-5: FKRE:64.92 ± 8.56, GFI:8.10 ± 1.13, FKGL:8.74 ± 1.73, SMOG:6.97 ± 1.26; ChatGPT-4o:65.44 ± 7.43, GFI:8.04 ± 1.48, FKGL:8.73 ± 1.80, SMOG:6.86 ± 1.53). Aggregating queries on a single page yielded higher accuracy, relevance, and comprehensiveness scores compared to isolated questioning (ChatGPT-4o: W = 4.451, p < 0.001; W = 4.356, p < 0.001; W = 1.965, p = 0.049. ChatGPT-5: W = 3.234, p < 0.001; W = 2.697, p = 0.007; W = 3.885, p < 0.001). ChatGPT-5 produces more concise and qualitatively superior responses than ChatGPT-4o. The patient prompt generated responses with high readability and strong empathy, and is therefore recommended for patient use. Consequently, aggregating related questions on a single page is advised to obtain higher-quality answers.

Blepharoptosis is a common eyelid disorder that impairs both vision and appearance, requiring accurate assessment for effective treatment. This study aimed to develop and evaluate a deep learning (DL)-based system for automatic measurement of eyelid and periorbital parameters and for classifying levator function (LF) in patients with blepharoptosis. We retrospectively included 1,177 patients who underwent ptosis surgery at a tertiary oculoplastic center from January 2016 to November 2021. LF was categorized into good (> 10 mm), fair (4-10 mm), and poor (≤ 4 mm) based on clinical evaluation. The DL model segmented eyelid and eyebrow regions and measured key parameters; manual measurements were performed for comparison. A multinomial logistic regression model incorporating DL-derived features and demographic data was used to predict LF grades. The DL system achieved high segmentation performance (Dice coefficient = 0.910) and strong agreement with manual measurements (ICC = 0.988 for MRD1; 0.902 for CBH). The regression model classified LF grades with an overall accuracy of 0.760 and an AUC of 0.829, within the range of ophthalmologist assessments (highest clinician accuracy = 0.767). This DL-based system offers an efficient, objective tool for periorbital assessment and LF grading, supporting personalized diagnosis and surgical planning in blepharoptosis management.

Risk communication represents a nuanced discourse within the healthcare sector, characterized by the topics' sensitivity and the potential for misunderstandings between healthcare providers and patients. This delicacy stems from the complexity of effectively conveying information about risks. Consequently, a primary obstacle lies in fostering healthcare providers' understanding of implicit communication nuances inherent in pre-operative risk discussions. This study aims to address this gap in the literature by examining the topic through the lens of the philosophy of language, specifically utilizing pragmatic analysis tools to elucidate implicit understandings in doctor-patient interactions. We employ this approach to scrutinize instances of pancreatic cancer diagnosis. Through empirical analysis of gathered data, we illustrate the inadequacies of current state-of-the-art models in accurately identifying misunderstandings within healthcare dialogues. In conclusion, we propose avenues for future research in this domain, underscoring the importance of further exploration into improving risk communication in healthcare settings.

Heart rate variability (HRV) is a well-established, noninvasive measure of autonomic nervous system activity and is associated with clinical outcomes. Although real-time monitoring of HRV is valuable in clinical practice, its effectiveness is often compromised by major challenges: high inter-individual variability and frequent data contamination from procedural artifacts. To address these challenges, we developed and validated a computational framework for robust and personalized real-time HRV analysis oriented toward clinical application. The framework performs simultaneous analysis and visualization of both time- and frequency-domain HRV indices and incorporates an adaptive alert algorithm that personalizes alert thresholds using the interquartile range of each patient's own data. A workflow-integrated mechanism for manually annotating and excluding artifact-prone periods prevents procedural artifacts from skewing the statistical baselines, and a multi-scale visualization module provides a unified view of short-term fluctuations and long-term trends. While existing HRV tools are powerful for research or offline analysis, they often lack the integration of personalized alerting and workflow-oriented artifact management needed for bedside care. The proposed system uniquely combines personalized alerting, care-linked artifact exclusion, and multi-scale bedside visualization within a single real-time software package. The framework was validated using open-access electrocardiogram (ECG) databases and synthetic noise-contaminated signals, confirming robust R-wave detection across pediatric and adult recordings and under low signal-to-noise conditions. In addition, the framework was operationally validated at the bedside using ECG data from 24 newborn patients. By systematically addressing the core challenges of personalization and artifact management in a clinically integrated manner, this work represents a significant step toward translating real-time HRV analysis into routine vital sign management and, ultimately, improved patient outcomes.

The ubiquitous availability of work-related applications on personal devices makes healthcare workers prone to working during leisure time. We tested the hypothesis that an intervention to reduce work-related screen time during a weekend off reduces stress in healthcare workers. Pragmatic parallel design randomized controlled trial between November 2021 and November 2023. Healthcare workers using a smartphone with a work email application were eligible. Randomization was 1:1 to no treatment or a threefold educational intervention to: 1) activate automated responses to emails received, 2) reduce screen time, and 3) uninstall work applications from personal devices. The primary outcome was the change in participants' stress from pre- to post-weekend, measured with the Perceived Stress Scale-10. The secondary outcome was device screen time. Among 815 enrolled participants, 520 responded to the post-intervention survey. The median [Q1, Q3] change from baseline Perceived Stress Scale-10 scores was -2 [-7, 0] in controls and -4 [-9, 0] in the intervention group. The mean difference (intervention - control) in post-intervention Perceived Stress Scale-10 scores, adjusted for baseline stress, was -1.6 (95% CI: -2.6, -0.6; P = 0.002). The median [Q1, Q3] change from baseline screen time was 0 [-2, 1] hours in the controls and -1 [-3, 0] hours in the intervention group. A three-pronged educational intervention targeting work-related screen time among healthcare workers doubled stress reduction during a non-work weekend. Stress reduction in the intervention group was mediated by reduced screen time. Future research should investigate long-term effects and broader implementation of such interventions to promote well-being in the healthcare workforce. Trial Registration: https://clinicaltrials.gov/study/NCT05106647 . Identifier: NCT05106647, Registration date: November 4, 2021.