Informatics in Medicine Unlocked最新文献

{"title":"Examining the association between genetic polymorphisms and osteoporosis among post-menopausal women: a systematic review","authors":"Zainab Alhalwachi ,&nbsp;Mira Mousa ,&nbsp;Salsabeel Juneidi ,&nbsp;Gabriela Restrepo-Rodas ,&nbsp;Spyridon Karras ,&nbsp;Habiba Alsafar ,&nbsp;Fatme Al Anouti","doi":"10.1016/j.imu.2025.101652","DOIUrl":"10.1016/j.imu.2025.101652","url":null,"abstract":"<div><h3>Purpose</h3><div>Postmenopausal osteoporosis (PMOP) is the most prevalent metabolic bone disease among women, characterized by significant bone density loss and increased fracture risk. With a genetic component, a systematic review was conducted on the association between genetic polymorphisms and PMOP risk.</div></div><div><h3>Methods</h3><div>A comprehensive review of PubMed literature examined genetic polymorphisms linked to PMOP risk. The primary outcome was to identify the most frequently studied genes linked to PMOP. The secondary outcome was to perform a meta-analysis on the top genetic markers to assess their overall association with PMOP risk.</div></div><div><h3>Results</h3><div>Six genes, accounting for 55.08 % of all studies, were strongly associated with PMOP. Of these, the <em>VDR</em> gene was featured in 35 articles (18.72 % of studies), TNFRSF11B in 23 (12.30 %), <em>ESR1</em> in 18 (9.63 %), <em>COL1A1</em> in 12 (6.42 %), <em>MTHFR</em> in 8 (4.27 %), and TGFb1 in 7 (3.74 %). Meta-analysis showed five markers significantly associated with PMOP: SNP rs1544410 (OR_G: 0.74 (0.59, 0.92)), SNP rs11568820 (OR_G: 1.40 (1.03, 1.91)), and SNP rs2228570 (OR_T: 1.39 (1.12, 1.73)) in the <em>VDR</em> gene; and PvuII variant (OR_P: 0.80 (0.67, 0.96)) in the <em>ESR1</em> gene.</div></div><div><h3>Conclusion</h3><div>This review strengthens the importance of conducting a robust, multi-ethnic, large cohort study with functional analysis to corroborate the findings of the six key genes associated with PMOP. Replicating these findings in larger and more diverse datasets is crucial to validate their biological relevance and potential clinical application.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101652"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-model deep learning approach for the classification of kidney diseases using medical images","authors":"Waleed Obaid ,&nbsp;Abir Hussain ,&nbsp;Tamer Rabie ,&nbsp;Dhafar Hamed Abd ,&nbsp;Wathiq Mansoor","doi":"10.1016/j.imu.2025.101663","DOIUrl":"10.1016/j.imu.2025.101663","url":null,"abstract":"<div><div>Renal impairment poses a risk across all ages. With the global nephrologist shortage, the rising public health concerns over kidney failure, and advancements in technology, there is a growing need for an AI system capable of identifying kidney anomalies automatically. Chronic kidney disease is marked by a gradual failure in kidney function due to various factors, such as stones, cysts, and tumors. Chronic kidney disease often presents without noticeable symptoms initially, leading to cases remaining untreated until advanced stages. Tumors, which are dense tissue masses, can directly harm organs, including glands and spinal cells. Kidney stone disease, or urolithiasis, occurs when many solids accumulate in the urinary tract, leading to stone formation. This research paper leveraged a deep learning approach to address the worldwide shortage of urologists by facilitating the detection of kidney diseases. A novel deep learning technique is proposed using Darknet53 for the classification of kidney diseases using a large dataset gathered from five resources. The total number of images is 27,145 scans of the entire abdomen and urogram, focusing on common kidney conditions, including stones, cysts, and tumors. The data was grouped into four classes: normal, cyst, tumor, and stone. The proposed technique involves the use of 16 deep-learning models to obtain enhanced performance based on accuracy, recall, specificity, and precision, offering new potential for detecting kidney abnormalities. Model performance was evaluated, achieving 99.69 %, 0.31 %, 99.66 %, 99.88 %, 99.77 %, 0.12 %, 99.71 %, 99.60 %, and 99.17 % for accuracy, error, recall, specificity, precision, false positive rate, F1_score, Matthews Correlation Coefficient, and Kappa, respectively. Our simulation results using the Fuzzy Decision by Opinion Score Method indicated that the Darknet53 generated the best results for detecting kidney abnormalities.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101663"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards trustworthy AI-driven leukemia diagnosis: A hybrid Hierarchical Federated Learning and explainable AI framework","authors":"Khadija Pervez ,&nbsp;Syed Irfan Sohail ,&nbsp;Faiza Parwez ,&nbsp;Muhammad Abdullah Zia","doi":"10.1016/j.imu.2025.101618","DOIUrl":"10.1016/j.imu.2025.101618","url":null,"abstract":"<div><div>Accurate detection and classification of microscopic cells from acute lymphoblastic leukemia remain challenging due to the difficulty of differentiating between cancerous and healthy cells. This paper proposes a novel approach to identify and categorize acute lymphoblastic leukemia that uses explainable artificial intelligence and federated learning to train models across multiple institutions while keeping patient information decentralized and encrypted. The framework trains EfficientNetB3 for the classification of leukemia cells and incorporates explainability techniques to make decisions of the underlying model transparent and interpretable. The framework employs a hierarchical federated learning approach that allows distributed learning across clinical centers, ensuring that sensitive patient data remain localized. Explainability techniques such as saliency maps, occlusion sensitivity, and randomized input sampling for explanation with relevant evaluation scores are integrated in the framework to provide visual and textual explanations of model’s predictions to enhance interpretability. The experiments were carried out on a publicly available dataset consisting of 15,135 microscopic images. The performance of the proposed model was benchmarked against traditional centralized models and classical federated learning techniques. The proposed model demonstrated a 2.5% improvement in accuracy (96.5%) and a 5.4% increase in F1-score (94.4%) compared to baseline models. Hierarchical federated learning reduced communication costs by 15% while maintaining data privacy. The integration of explainable artificial intelligence improved the transparency of model decisions, with a high area under the ROC curve (AUC) of 0.98 for the classification of leukemia cells. These results suggest that the proposed framework offers a robust solution for intelligent systems for medical diagnostics and can also be extended to other medical imaging tasks.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101618"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Customer relationship management systems in clinical laboratories: A systematic review","authors":"Jonel Bation ,&nbsp;Mary Ann Jaro ,&nbsp;Lheyniel Jane Nery ,&nbsp;Mudjahidin Mudjahidin ,&nbsp;Andre Parvian Aristio ,&nbsp;Eddie Bouy Palad ,&nbsp;Jason Chavez ,&nbsp;Lemuel Clark Velasco","doi":"10.1016/j.imu.2025.101628","DOIUrl":"10.1016/j.imu.2025.101628","url":null,"abstract":"<div><div>The implementation of Customer Relationship Management (CRM) Systems in clinical laboratories is crucial in improving customer relationships, service quality, and operational efficiency that aligns with a patient-centric care model. This study utilizes the PRISMA guidelines in reviewing and synthesizing 26 journal articles using the People, Process, Technology (PPT) framework to analyze the roles of people involved in clinical settings, the processes by which laboratory services were delivered, and the technological considerations enhancing patient care. Results revealed that the successful implementation of CRM systems in clinical laboratories depends on the aligned efforts of both developers and end-users. Subsequently, marketing processes and customer service were then found out to be crucial for the successful utilization of CRM systems in clinical laboratories. The features and the system integration techniques of CRM systems were found out to be vital in developing efficient operations, enhancing data analysis, and extending accessibility. The research gap analysis, on the one hand, shows that the effectiveness of CRM systems on the patients, the lack of qualitative methods, and the development of corrective actions to increase patient satisfaction are relevant areas of research concerns to optimize the effectiveness of implementing different CRM systems.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101628"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing the Sensitivity Frontier in digital contact tracing: Comparative analysis of proposed methods toward maximized utility","authors":"Junko Ami ,&nbsp;Yanbo Pang ,&nbsp;Hiroshi Masui ,&nbsp;Takashi Okumura ,&nbsp;Yoshihide Sekimoto","doi":"10.1016/j.imu.2025.101622","DOIUrl":"10.1016/j.imu.2025.101622","url":null,"abstract":"<div><div>During the COVID-19 pandemic, many countries adopted Digital Contact Tracing (DCT) technology to control infections. However, the widely-used Bluetooth Low Energy (BLE)-based DCT requires both the infected individual and the contact to have the application activated to detect exposure. Forcing citizens to install the DCT application could compromise their privacy. Therefore, to make DCT a truly usable tool, it is crucial to develop a DCT system that possesses high sensitivity, without depending on the application usage rate.</div><div>The Computation of Infection Risk via Confidential Locational Entries (CIRCLE) is a DCT method that utilizes connection logs from mobile phone base stations, theoretically offering much higher sensitivity than BLE-based DCT. However, its real performance has not been proven, and thus, this paper estimates the sensitivity and specificity of both BLE-based DCT and CIRCLE in a comparative setting. The estimation combines simulated movement patterns of residents with real-world data from app usage in Japan, utilizing both simulation and numerical modeling, with missing data supplemented through sensitivity analysis.</div><div>The sensitivity of BLE-based DCT is severely limited by the application’s usage rate, with an estimated baseline of just 10.9%, and even under highly optimistic assumptions, it only reaches 27.0%. In contrast, CIRCLE demonstrated a significantly higher sensitivity of 85.6%, greatly surpassing BLE-based DCT. The specificity of CIRCLE, though, decreased as the number of infected individuals increased, dropping to less than half of BLE-based DCT’s specificity during widespread infection. The BLE-based DCT used during the pandemic suffers from low sensitivity. While CIRCLE has specificity challenges, it provides exceptionally high sensitivity. Integrating these methods could redefine the design of digital contact tracing, leading to better utility for future infection control.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101622"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Fuzzy C-Means clustering and PCA in public health: A machine learning approach to combat CVD and obesity","authors":"Gamal Saad Mohamed Khamis ,&nbsp;Nasser S. Alqahtani ,&nbsp;Sultan Munadi Alanazi ,&nbsp;Mohammed Muharrab Alruwaili ,&nbsp;Mariam Shabram Alenazi ,&nbsp;Maneaf A. Alrawaili","doi":"10.1016/j.imu.2025.101666","DOIUrl":"10.1016/j.imu.2025.101666","url":null,"abstract":"<div><div>This study introduces a novel framework that integrates principal component analysis (PCA) with fuzzy c-means (FCM) clustering to enhance the analysis of high-dimensional health data, specifically targeting the identification of at-risk groups for cardiovascular disease (CVD) and obesity. This unique approach, which has not been previously explored in public health, promises to provide new insights and solutions to these pressing health issues.</div><div>The proposed PCA-FCM model was applied to a dataset comprising more than 20 health variables from a population sample aged 18–75 years. The analysis identified four distinct clusters, each showing unique risk patterns. For instance, Cluster One (mean age, 29) showed elevated body mass index (BMI) (mean, 33.7 kg/m²), high waist circumference (113 cm), and signs of insulin resistance (FBS, 133 mg/dL; HOMA-IR, 7.12). In contrast, Cluster Two (mean age, 61) exhibited the highest systolic blood pressure (SBP, 143 mmHg), elevated LDL cholesterol (4.27 mmol/L), and triglycerides (2.59 mmol/L), indicating advanced metabolic syndrome. Cluster Three (mean age, 51) presented a healthier metabolic profile with lower HOMA-IR (3.74), normal SBP (127 mmHg), and balanced lipid levels (HDL, 1.36 mmol/L). Cluster Four (mean age, 43) showed elevated SBP (134 mmHg), BMI (32.1 kg/m²), and HOMA-IR (6.05), suggesting a latent risk group.</div><div>PCA identified waist circumference, visceral fat, LDL/HDL ratio, non-HDL cholesterol, and waist-to-height ratio as the most influential variables contributing to cluster separation, with loadings above 0.70 on the first two principal components. Meanwhile, exercise, height, family history, and HDL had loadings below 0.30, indicating minimal influence on cluster formation.</div><div>The model evaluation supported the selection of the four-cluster solution, with a Silhouette Score of 0.62 and Between-Cluster Variation accounting for 64 % of the total variance, signifying well-defined and cohesive clusters.</div><div>Although this framework enhances clustering precision and uncovers clinically actionable patterns, challenges such as health data privacy concerns, clinicians’ difficulty in interpreting PCA results, validating model generalizability across diverse populations, and technical resource limitations in low-resource settings must be addressed for successful implementation in real-world healthcare systems. Future research should incorporate longitudinal data and explore integration with advanced models, such as deep learning, to improve predictive accuracy and adaptability in real-time clinical environments.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101666"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LMVT: A hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis","authors":"Jesika Debnath ,&nbsp;Al Shahriar Uddin Khondakar Pranta ,&nbsp;Amira Hossain ,&nbsp;Anamul Sakib ,&nbsp;Hamdadur Rahman ,&nbsp;Rezaul Haque ,&nbsp;Md.Redwan Ahmed ,&nbsp;Ahmed Wasif Reza ,&nbsp;S M Masfequier.Rahman Swapno ,&nbsp;Abhishek Appaji","doi":"10.1016/j.imu.2025.101669","DOIUrl":"10.1016/j.imu.2025.101669","url":null,"abstract":"<div><div>Lung cancer continues to be a leading cause of cancer-related deaths worldwide due to its high mortality rate and the complexities involved in diagnosis. Traditional diagnostic approaches often face issues such as subjectivity, class imbalance, and limited applicability across different imaging modalities. To tackle these problems, we introduce Lung MobileVIT (LMVT), a lightweight hybrid model that combines a Convolutional Neural Network (CNN) and a Transformer for multiclass lung cancer classification. LMVT utilizes depthwise separable convolutions for local texture extraction while employing multi-head self-attention (MHSA) to capture long-range global dependencies. Furthermore, we integrate attention mechanisms based on the Convolutional Block Attention Module (CBAM) and feature selection techniques derived from the Simple Gray Level Difference Method (SGLDM) to improve discriminative focus and minimize redundancy. LMVT utilizes attention recalibration to enhance the saliency of the minority class, while also incorporating curriculum augmentation strategies that balance representation across underrepresented classes. The model has been trained and validated using two public datasets (IQ-OTH/NCCD and LC25000) and evaluated for both 3-class and 5-class classification tasks. LMVT achieved an impressive 99.61 % accuracy and 99.22 % F1-score for the 3-class classification, along with 99.75 % accuracy and 99.44 % specificity for the 5-class classification. This performance surpasses that of several recent Vision Transformer (ViT) architectures. Statistical significance tests and confidence intervals confirm the reliability of these performance metrics, while an analysis of model complexity supports its capability for potential deployment. To enhance clinical interpretability, the model is integrated with explainable AI (XAI) and is implemented within a web-based diagnostic application for analyzing CT and histopathology images. This study highlights the potential of hybrid ViT architectures in creating scalable and interpretable data-driven tools for practical use in lung cancer diagnostics.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101669"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing heart attack prognosis: Introducing an innovative regression model for prediction","authors":"Hanaa Albanna ,&nbsp;Madhav Raj Theeng Tamang ,&nbsp;Chandan Patel ,&nbsp;Mhd Saeed Sharif","doi":"10.1016/j.imu.2025.101664","DOIUrl":"10.1016/j.imu.2025.101664","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective:&lt;/h3&gt;&lt;div&gt;Heart attack prediction using machine learning is crucial for preemptive action and personalized healthcare. This research aims to predict heart attacks by employing machine learning in healthcare using a diverse range of patient data-including demographic, lifestyle, and physiological factors, which helps to create robust and generalizable predictions. Besides this, various models that balance accuracy with interpretability have been presented, emphasizing early detection and proactive intervention. It is expected that this cross-disciplinary approach will underline the role of machine learning in the mitigation of the heart disease burden and optimization of resources spent on healthcare.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;This study explores the application of machine learning techniques for predicting heart attack risk using structured clinical data. A range of classification models — Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) — were selected based on their proven effectiveness in prior healthcare prediction studies and their balance between accuracy and interpretability. The methodology involved comprehensive data preprocessing, class imbalance handling, and hyperparameter tuning to optimize model performance. Performance metrics included Accuracy, Precision, Recall, F1-score, and AUC-ROC. Exploratory Data Analysis (EDA) was conducted to assess the role of variables such as BMI, age, and glucose levels in predicting stroke, a proxy used for heart attack due to dataset limitations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;The SVM and LR models achieved the highest accuracy (95.08%), followed by RF (94.86%) and DT (91.46%). Despite high accuracy, key challenges were observed:&lt;/div&gt;&lt;div&gt;Class Imbalance: Only 249 cases in the dataset represented positive stroke outcomes, resulting in poor recall for minority class predictions. This reduced the model’s sensitivity to actual stroke cases, a significant limitation in clinical scenarios where false negatives can be life-threatening.&lt;/div&gt;&lt;div&gt;Data-Label Inconsistency: Although the study is framed as predicting heart attacks, the dataset pertains to stroke prediction. This misalignment creates confusion in the clinical relevance of the findings and weakens the generalizability of the models for heart attack risk assessment.&lt;/div&gt;&lt;div&gt;Lack of Model Interpretability in Practice: Though LIME and SHAP were cited as tools to ensure model transparency, they were not implemented or evaluated. This limits clinicians’ trust in the model’s predictions—an essential factor for real-world adoption.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion:&lt;/h3&gt;&lt;div&gt;This research shows how machine learning can play a meaningful role in improving how we predict heart attacks and ultimately help improve patient care. The results demonstrated that even well-known models like Support Vector Machine and Logistic Regression can perform very well when applied to s","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101664"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usability and accessibility in mHealth stroke apps: An empirical assessment","authors":"Pontus Svensson ,&nbsp;Shuanglan Lin ,&nbsp;Leonardo Horn Iwaya","doi":"10.1016/j.imu.2025.101616","DOIUrl":"10.1016/j.imu.2025.101616","url":null,"abstract":"<div><h3>Background</h3><div>Cerebrovascular accidents or strokes continue to be among the leading causes of death and disability worldwide. This has stressed the need to design digital health solutions that can be effectively used by patients, caregivers, and medical professionals, helping to alleviate the global disease burden. In this context, mobile health (mHealth) apps are shown to be valuable solutions for bridging healthcare gaps.</div></div><div><h3>Objective</h3><div>In this study, we aim to evaluate the quality aspects of usability and accessibility of stroke-related mHealth apps for Android. We seek to identify prevalent issues and discuss recommendations to enhance user experience and app quality.</div></div><div><h3>Methods</h3><div>We selected 16 mHealth stroke apps, accounting for more than 219k downloads. The apps were assessed through different methods, including accessibility testing with the Google Accessibility Scanner, overall quality assessment with the Mobile Application Rating Scale (MARS), and usability testing using heuristic evaluations.</div></div><div><h3>Results</h3><div>Our findings show significant issues with the apps’ touch target sizes and text contrast, which are particularly important for stroke app users with impaired vision and motor skills. MARS evaluations revealed that some apps, such as the Constant Therapy app, excelled in engagement and functionality. In contrast, many apps scored lower due to limited functionality and unclear/confusing interfaces, such as Stroke Recovery Predictor and Conversation Therapy Lite. Heuristic evaluations also highlighted several usability violations, such as a lack of “Visibility of System Status” and “Insufficient Error Messaging.”</div></div><div><h3>Conclusion</h3><div>Overall, most apps presented deficiencies in several aspects of usability and accessibility. As recommendations, developers can increase touch target sizes, improve text contrast, increase functional variety, optimise navigation, and enhance user engagement strategies. Addressing such issues can help improve the stroke apps’ usability and accessibility, aiming for better health outcomes for stroke patients.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101616"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient2Trial: From patient to participant in clinical trials using large language models","authors":"Surabhi Datta ,&nbsp;Kyeryoung Lee ,&nbsp;Liang-Chin Huang ,&nbsp;Hunki Paek ,&nbsp;Roger Gildersleeve ,&nbsp;Jonathan Gold ,&nbsp;Deepak Pillai ,&nbsp;Jingqi Wang ,&nbsp;Mitchell K. Higashi ,&nbsp;Lizheng Shi ,&nbsp;Percio S. Gulko ,&nbsp;Hua Xu ,&nbsp;Chunhua Weng ,&nbsp;Xiaoyan Wang","doi":"10.1016/j.imu.2025.101615","DOIUrl":"10.1016/j.imu.2025.101615","url":null,"abstract":"<div><h3>Purpose</h3><div>Large language models (LLMs) exhibit promising language understanding and generation capabilities and have been adopted for various clinical use cases. Investigating the feasibility of leveraging LLMs in building a clinical trial retrieval system for patients is crucial as it can greatly enhance the patient enrollment process by prioritizing the most suitable trials pertaining to a patient. In this work, we develop an LLM-assisted system focused on a patient-initiated approach, allowing patients with specific conditions to directly find eligible trials by completing disorder-specific questionnaires.</div></div><div><h3>Methods</h3><div>We obtained clinical trial eligibility criteria (from ClinicalTrials.gov) and simulated patient questionnaires (or topics) from the Text REtrieval Conference (TREC) 2023 Clinical Trials Track conducted by the National Institute of Standards and Technology (NIST), in which we also participated. These topics cover eight disorders across diverse domains, namely glaucoma, anxiety, chronic obstructive pulmonary disease, breast cancer, Covid-19, rheumatoid arthritis, sickle cell anemia, and type 2 diabetes. A Generative Pre-trained Transformer model (GPT-4) was employed for system development. We conducted both quantitative and qualitative evaluation using 37 patient topics.</div></div><div><h3>Results</h3><div>The system achieved an overall Precision@10 (proportion of relevant trials) of 0.7351 and NDCG@10 (considers ranking order of relevant trials) of 0.8109, indicating its effectiveness in retrieving ranked lists of suitable trials for patients. Notably, for eight out of 37 patient topics, all the top 10 retrieved trials were relevant. The system scored the highest on breast cancer (NDCG@10 = 0.9347, Precision@10 = 0.84) and the lowest on type 2 diabetes (NDCG@10 = 0.61, Precision@10 = 0.475). One probable reason could be that the information in breast cancer topics is relatively straightforward to match. Qualitative error analysis classified errors into four categories (e.g., difficulty in correctly matching inclusion criteria) and further highlighted strengths (e.g., ability to make clinical inference).</div></div><div><h3>Conclusion</h3><div>We demonstrated the feasibility of integrating LLMs in identifying and ranking suitable trials for patients across multiple disorders. Further work is required to assess the system's generalizability on other disorders and patient information sources. This system has the potential to expedite the patient-trial matching process by suggesting a ranked list of applicable trials to patients and clinicians.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101615"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}