DIGITAL HEALTH最新文献

{"title":"Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach.","authors":"Xingyu Liu, Jiali Mo, Zuting Liu, Yanqiu Ge, Tian Luo, Jie Kuang","doi":"10.1177/20552076251333568","DOIUrl":"https://doi.org/10.1177/20552076251333568","url":null,"abstract":"<p><strong>Background: </strong>Stroke-associated pneumonia (SAP) is a major cause of mortality following ischemic stroke (IS). However, existing predictive models for SAP often lack transparency and interpretability, limiting their clinical utility. This study aims to develop an interpretable Bayesian network (BN) model for predicting SAP in IS patients, focusing on enhancing both predictive accuracy and clinical interpretability.</p><p><strong>Methods: </strong>This retrospective study included patients diagnosed with IS and admitted to the Second Affiliated Hospital of Nanchang University between January and December 2019. Clinical data collected within 48 h of admission and SAP occurrences within 7 days were analyzed. Dimensionality reduction was performed using Least Absolute Shrinkage and Selection Operator regression, while data imbalances were addressed using synthetic minority oversampling technique. A BN model was trained using a hill-climbing algorithm and compared to logistic regression, decision trees, deep neural networks, and existing risk-scoring systems. Decision curve analysis was used to assess clinical usefulness.</p><p><strong>Results: </strong>Of the 1252 patients, 165 (13.18%) patients had SAP within 7 days of admission. The BN model identified age, risk of pressure injury (PI), National Institutes of Health Stroke Scale (NIHSS) score, and C-reactive protein (CRP) as significant prognostic factors. The BN model achieved an area under the curve of 0.85(95% CI: 0.78-0.92) on the test set, outperforming other models and demonstrating a greater net benefit in clinical decision-making.</p><p><strong>Conclusions: </strong>Age, risk of PI, NIHSS score, and CRP are significant predictors of SAP in IS patients. The interpretable BN model demonstrates superior predictive performance and interpretability, suggesting its potential as an effective and interpretable tool for clinical decision support in SAP risk assessment.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251333568"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The mechanism of word-of-mouth learning on chronic disease patients' physician choice in online health communities: Latent Dirichlet allocation analyses and cross-sectional study.","authors":"Linlin Han, Narongsak Tek Thongpapanl, Ou Li","doi":"10.1177/20552076251332685","DOIUrl":"https://doi.org/10.1177/20552076251332685","url":null,"abstract":"<p><strong>Background: </strong>Word-of-mouth learning (WOML) plays a substantial role in patients' physician choice behavior. However, there is still a research gap in analyzing the mechanism of WOML on chronic disease patients' physician choice in online health communities (OHCs) considering individual differences.</p><p><strong>Objective: </strong>This study aims to develop a physician choice mechanism research model to reveal the influence of WOML on chronic disease patients' physician choice decision process from external interaction to internal cognition and emotion in OHCs based on social learning theory (SLT). The moderating effects of reasons for consultation and patients' demographic characteristics on the model's relationships were also explored.</p><p><strong>Methods: </strong>Guided by SLT, this study identified the external interaction factors and internal cognitive and emotional factors by analyzing 72,123 patients' online reviews based on a Latent Dirichlet Allocation model and developed the physician choice mechanism research model. The model was validated using structural equation modeling based on an online questionnaire survey of 526 valid Chinese patients with chronic disease. The moderating effect of reasons for medical consultation and demographic characteristics was examined using multi-group analysis.</p><p><strong>Results: </strong>Status capital (SC), decisional capital (DC), and price value (PV)) were the main external interaction factors to initiating chronic disease patients' internal cognition and emotion (perceived convenience (PC), perceived health benefits (PH), and patients' physician choice intention (CI)). PH and PC significantly mediated the relationship between SC, DC, PV, and CI. Reasons for medical consultation, district, and sex significantly moderated the relationships in the model.</p><p><strong>Conclusions: </strong>Considering individual differences, the results of this study advance a comprehensive understanding of how chronic disease patients interact with the environment through WOML to make physician choice decisions. OHCs can recommend suitable physician information to chronic disease patients considering individual differences to match patients' demands and improve service quality.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251332685"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging.","authors":"JaeYoung Kang, JunYoung Park, YoungJae Kim, BumJoon Kim, SangHee Ha, KwangGi Kim","doi":"10.1177/20552076251334040","DOIUrl":"https://doi.org/10.1177/20552076251334040","url":null,"abstract":"<p><strong>Background: </strong>Rapid and accurate identification of large vessel occlusion (LVO) is crucial for determining eligibility for endovascular treatment. We aimed to validate whether computed tomography combined with clinical information (CT&CI) or diffusion-weighted imaging (DWI) offers better predictive accuracy for anterior circulation LVO.</p><p><strong>Methods: </strong>Computed tomography combined with clinical information and DWI data from patients diagnosed with acute ischemic stroke were collected. Three deep-learning models, convolutional neural network, EfficientNet-B2, and DenseNet121, were used to compare CT&CI and DWI for detecting anterior circulation LVO.</p><p><strong>Results: </strong>A total of 456 patients, 228 patients with LVO [68.91 ± 12.84 years, 63.60% male; initial National Institutes of Health Stroke Scale (NIHSS) score: median 11 (7-14)] and without LVO [67.06 ± 12.29 years, 64.04% male; initial NIHSS score: median 2 (1-4)] were enrolled. Diffusion-weighted imaging achieved better results than CT&CI did in each performance metric. In DenseNet121, the area under the curves (AUCs) were found to be 0.833 and 0.756, respectively, while in EfficientNet-B2, the AUCs were 0.815 and 0.647, respectively.</p><p><strong>Conclusions: </strong>In detecting the presence of anterior circulation LVO, DWI showed better results in each performance metric than CT&CI did, and the best-performing deep-learning model was DenseNet121.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251334040"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inertial sensor-based heel strike and energy expenditure prediction using a hybrid machine learning approach.","authors":"Kethohalli R Vidyarani, Viswanath Talasila, Raafay Umar, Venkatesan Prem, Ravi Prasad K Jagannath, Gurusiddappa R Prashanth","doi":"10.1177/20552076251333375","DOIUrl":"https://doi.org/10.1177/20552076251333375","url":null,"abstract":"<p><strong>Objective: </strong>Gait analysis plays a critical role in healthcare, biomechanics, and sports science, particularly for estimating energy expenditure (EE). This study introduces a hybrid machine learning approach integrating convolutional neural networks (CNNs), long-short-term memory (LSTM) networks, and transfer learning (TL) to estimate volume of oxygen (VO₂) and detect heel strikes (HS) using data from a single 9-axis inertial measurement unit (IMU).</p><p><strong>Methods: </strong>A clinical-grade VO₂ machine provided reference data for model training. The hybrid model was designed to combine spatial and temporal feature extraction capabilities from CNNs and LSTM networks while leveraging pre-trained weights through TL. The study compared the performance of the hybrid model with an LSTM-only approach to quantify improvements in VO₂ prediction.</p><p><strong>Results: </strong>The hybrid model significantly reduced the VO₂ prediction error from 20% to 3% compared to using LSTM-only approach. Additionally, the model demonstrated high accuracy for HS detection, achieving 93.53% accuracy as indicated by training and validation results. The lightweight IMU-based system proved effective for VO₂ estimation, offering a practical alternative to traditional VO₂ measurement systems, which are often complex, bulky, and uncomfortable for subjects.</p><p><strong>Conclusions: </strong>This study highlights the potential of a hybrid machine learning approach using IMU-based systems for accurate VO₂ estimation and HS detection. While the results are promising, the model's performance is constrained by 10 healthy subject datasets. Future work will require validation with more diverse datasets to enhance generalizability and robustness.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251333375"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing patient confidence in telehealth: Comparing across 17 medical specialties.","authors":"Abdulrahman M Jabour","doi":"10.1177/20552076251330486","DOIUrl":"https://doi.org/10.1177/20552076251330486","url":null,"abstract":"<p><strong>Background: </strong>Telehealth has become an increasingly vital component of healthcare delivery, particularly after the COVID-19 pandemic. As telemedicine expands its reach, understanding patient confidence in using telehealth services across different medical specialties is crucial for its continued adoption.</p><p><strong>Objective: </strong>This study aims to address this need by exploring patient confidence in telemedicine for various health conditions.</p><p><strong>Methods: </strong>A cross-sectional study was conducted using an online self-administered survey. We collected data from 390 respondents, examining demographic information, patient confidence levels, and factors influencing their confidence. The survey included 27 questions and focused on health conditions where telehealth is most applicable, such as chronic disease management, mental health services, and preventive care. Participants rated their confidence in using telehealth for various conditions on a 5-point Likert scale.</p><p><strong>Results: </strong>The overall confidence score averaged 3.3 (SD = 0.713), indicating moderate confidence among participants. The Friedman test revealed significant variability in patients' reported confidence levels regarding the use of telehealth across different medical specialties. General consultations were rated the highest (mean rank = 11.97), while emergency scenarios received the lowest confidence ratings (mean rank = 6.19).</p><p><strong>Conclusions: </strong>These findings suggest that patient confidence in telehealth varies significantly across different medical scenarios, emphasizing the need for tailored telehealth strategies to address specific patient concerns and improve confidence in its use. Developing specialty-specific guidelines and policies can further support the effective integration and adoption of telehealth in the delivery of care.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251330486"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The intention to use short videos for health information among Chinese adults: Based on the technology acceptance model.","authors":"Junjiang Liu, Rui Huang, Jinluan Ren, Peifan Li, Pengfei Wang","doi":"10.1177/20552076251335519","DOIUrl":"https://doi.org/10.1177/20552076251335519","url":null,"abstract":"<p><strong>Objective: </strong>The dissemination of health information plays a crucial role in health promotion. With the evolution of the Internet, short videos have become a significant medium for health information dissemination. This study aims to examine how short video features influence users' intentions for health information and provide actionable recommendations for improving health communication strategies.</p><p><strong>Methods: </strong>By integrating trust and perceived interactivity into the technology acceptance model, this study introduces a modified acceptance model for short videos. The model was evaluated through a survey conducted among 435 Chinese adults aged 18 and above, who had prior experience with short videos. The analysis was carried out using the partial least squares structural equation modeling technique for parameter estimation and model validation.</p><p><strong>Results: </strong>The analysis revealed that the model accounts for 18.3% of the variance in perceived usefulness, 34.9% in attitudes towards the videos, and 26.4% in the intention to use them. Significant positive associations on usage intention were observed from trust (β = 0.184, <i>P</i> < 0.001), perceived interactivity (β = 0.247, <i>P</i> < 0.001), and attitude (β = 0.210, <i>P</i> < 0.001). Perceived usefulness (β = 0.240, <i>P</i> < 0.001), perceived ease of use (β = 0.213, <i>P</i> < 0.001), trust (β = 0.173, <i>P</i> < 0.001), and perceived interactivity (β = 0.152, <i>P</i> = 0.001) were found to have significant positive associations with attitude.</p><p><strong>Conclusions: </strong>Perceived usefulness, ease of use, trust, and perceived interactivity emerged as strong predictors of usage intention, with attitude serving as a mediating factor in the relationship between trust, perceived interactivity, and usage intention. These findings highlight the importance of creating authoritative, user-friendly, and engaging content of short videos to enhance health information dissemination effectiveness and credibility.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251335519"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease: A secondary analysis of cross-sectional national survey.","authors":"Sudarshan Pant, Hyung Jeong Yang, Sehyun Cho, EuiJeong Ryu, Ja Yun Choi","doi":"10.1177/20552076251333660","DOIUrl":"https://doi.org/10.1177/20552076251333660","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop and validate a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease (COPD) using data from a national survey.</p><p><strong>Methods: </strong>Data from the Korea National Health and Nutrition Examination Survey (2007-2018) were used to extract 5466 COPD-eligible cases. The data collection involved demographic, behavioral, and clinical variables, including 21 predictors such as age, sex, and pulmonary function test results. The dependent variable, smoking status, was categorized as smoker or nonsmoker. A residual neural network (ResNN) model was developed and compared with five machine learning algorithms (random forest, decision tree, Gaussian Naive Bayes, K-nearest neighbor, and AdaBoost) and two deep learning models (multilayer perceptron and TabNet). Internal validation was performed using five-fold cross-validation, and model performance was evaluated using the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, and F1-score.</p><p><strong>Results: </strong>The ResNN achieved an AUROC, sensitivity, specificity, and F1-score of 0.73, 70.1%, 75.2%, and 0.67, respectively, outperforming previous machine learning and deep learning models in predicting smoking status in patients with COPD. Explainable artificial intelligence (Shapley additive explanations) identified key predictors, including sex, age, and perceived health status.</p><p><strong>Conclusion: </strong>This deep learning model accurately predicts smoking status in patients with COPD, offering potential as a decision-support tool to detect high-risk persistent smokers for targeted interventions. Future studies should focus on external validation and incorporate additional behavioral and psychological variables to improve its generalizability and performance.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251333660"},"PeriodicalIF":2.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Five steps for the deployment of artificial intelligence-driven healthcare delivery for remote and indigenous populations in Canada.","authors":"Amal Khan, Sandro Galea, Ivar Mendez","doi":"10.1177/20552076251334422","DOIUrl":"https://doi.org/10.1177/20552076251334422","url":null,"abstract":"<p><p>The integration of artificial intelligence (AI) into healthcare delivery offers transformative potential, especially for remote and underserved populations. In rural and remote regions like northern Saskatchewan, Canada, where Indigenous communities face elevated rates of chronic conditions such as diabetes and limited access to healthcare, AI-driven virtual care can bridge critical gaps. However, a universal approach falls short of addressing the unique needs of diverse populations. This communication outlines a five-step framework to guide AI-facilitated healthcare delivery tailored to community-specific demographics and clinical priorities. Steps include building comprehensive community profiles, assessing digital readiness, prioritizing healthcare needs, deploying culturally sensitive virtual care programs, and evaluating outcomes with <b>AI-powered analytics</b>. By leveraging AI in a systematic and inclusive manner, this approach addresses social determinants of health, improves equity, and enhances healthcare quality, offering a scalable model to improve health outcomes in geographically and demographically diverse settings.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251334422"},"PeriodicalIF":2.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring artificial intelligence for healthcare from the health professionals' perspective: The case of limited resource settings.","authors":"Mulugeta Desalegn Kasaye, Amare Gebrie Getahun, Mulugeta Hayelom Kalayou","doi":"10.1177/20552076251330552","DOIUrl":"https://doi.org/10.1177/20552076251330552","url":null,"abstract":"<p><strong>Introduction: </strong>Although artificial intelligence (AI) can boost clinical decision-making, personalize patient treatment, and advance the global health sectors, there are unique implementation challenges and considerations in developing countries. The perceptions, attitudes, and behavioral factors among the users are limitedly identified in Ethiopia.</p><p><strong>Objective: </strong>This study aimed to explore AI in healthcare from the perspectives of health professionals in a resource-limited setting.</p><p><strong>Methods: </strong>We employed a cross-sectional descriptive study including 404 health professionals. Data were collected using a self-structured questionnaire. A simple random sampling technique was applied. We used SPSS to analyze data. Tables and graphs were used to present the findings.</p><p><strong>Results: </strong>A 95.7% response rate was reported. The mean age of the respondents was 32.57 ± 5.34 SD. Almost 254 (62.9%) of the participants were Bachelors of Science degree holders. Nearly 156 (38.6%) of the participants were medical doctors. More than 50% (52.2%) of them said AI would be applicable for diagnosis and treatment purposes in healthcare organizations. This study identified that a favorable attitude, good knowledge, and formal training regarding AI technologies would foster clinical decision-making practices more efficiently and accurately. Similarly, our study also identified the potential barriers to AI technologies in healthcare such as ethical issues, privacy and security of patient data were some to mention.</p><p><strong>Conclusions: </strong>Our study revealed that positive attitude, good knowledge, and formal training are crucial to advance healthcare using AI technologies. In addition, this study identified self-reported AI concerns of the participants such as; privacy and security of data, ethical issues, and accuracy of AI systems. Attention could be given to overcome the barriers of AI systems in the health system. Providing training, allocating time to practice AI tools, incorporating AI courses in the curricula of medical education, and improving knowledge can further the usage of AI systems in healthcare settings.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251330552"},"PeriodicalIF":2.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The current status and future directions of artificial intelligence in the prediction, diagnosis, and treatment of liver diseases.","authors":"Bo Gao, Wendu Duan","doi":"10.1177/20552076251325418","DOIUrl":"https://doi.org/10.1177/20552076251325418","url":null,"abstract":"<p><p>Early detection, accurate diagnosis, and effective treatment of liver diseases are of paramount importance for improving patient survival rates. However, traditional methods are frequently influenced by subjective factors and technical limitations. With the rapid progress of artificial intelligence (AI) technology, its applications in the medical field, particularly in the prediction, diagnosis, and treatment of liver diseases, have drawn increasing attention. This article offers a comprehensive review of the current applications of AI in hepatology. It elaborates on how AI is utilized to predict the progression of liver diseases, diagnose various liver conditions, and assist in formulating personalized treatment plans. The article emphasizes key advancements, including the application of machine learning and deep learning algorithms. Simultaneously, it addresses the challenges and limitations within this domain. Moreover, the article pinpoints future research directions. It underscores the necessity for large-scale datasets, robust algorithms, and ethical considerations in clinical practice, which is crucial for facilitating the effective integration of AI technology and enhancing the diagnostic and therapeutic capabilities of liver diseases.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251325418"},"PeriodicalIF":2.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}