International Journal of Medical Informatics最新文献

{"title":"Ethics in Danish healthcare AI policy: A document analysis.","authors":"Victor Vadmand Jensen, Marianne Johansson Jørgensen, Rikke Hagensby Jensen, Jeppe Lange, Jan Wolff, Mette Terp Høybye","doi":"10.1016/j.ijmedinf.2025.106065","DOIUrl":"10.1016/j.ijmedinf.2025.106065","url":null,"abstract":"<p><strong>Introduction: </strong>Nations are increasingly turning towards artificial intelligence (AI) systems to support healthcare settings. While nations must then contend with ethical considerations surrounding healthcare AI, they do so in a variety of ways, emphasizing different ethical considerations in different ways. However, there is still limited knowledge on how Scandinavian healthcare AI policy emphasizes ethics. In this paper, we investigate ethics in Danish healthcare AI policy to highlight underlying policy preferences.</p><p><strong>Methods: </strong>We present a document analysis of Danish policy documents relating to AI. We view policy documents' contents as expectations that signal and frame what is perceived as a desirable future with healthcare AI. From 210 policy documents, we extracted data of text snippets related to categories of ethical principles and pipeline stages, as well as articulated reasons for considering ethics. We analyzed the proportions of ethical principles and pipeline stages quantitatively and reasons for considering ethics inductively.</p><p><strong>Results: </strong>The most frequently cited ethical principle was prevention of harm (n = 177), while the most commonly referenced pipeline stage was implementation, evaluation, and oversight (n = 189). Both ethical principles and pipeline stages significantly deviated from equal proportions (p<0.001). Additionally, five primary reasons for addressing ethics emerged in the documents: fit of AI with existing healthcare structures, the potential consequences of AI, its marketability, associated uncertainties, and the perceived inevitability of its adoption. These findings indicate that Danish healthcare AI policy predominantly frames ethical considerations based on the potential consequences of AI deployment.</p><p><strong>Conclusions: </strong>Our study suggests the need for steering Danish, and more broadly Scandinavian, healthcare AI policy toward other views of ethics that integrate non-potentiality.</p>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"204 ","pages":"106065"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a machine learning-based prediction model for in-ICU mortality in severe pneumonia: A dual-center retrospective study.","authors":"JunYing Niu, XiaoJie Lv, Lin Gao, HaoRan Jia, Jing Zhao","doi":"10.1016/j.ijmedinf.2025.106075","DOIUrl":"10.1016/j.ijmedinf.2025.106075","url":null,"abstract":"<p><strong>Introduction: </strong>Severe pneumonia (SP) carries a high risk of death in the intensive care unit (ICU). There is a paucity of effective assessment tools for ICU mortality in clinical practice. Therefore, this dual-centre study collects common clinical characteristics, develops, and externally validates machine learning (ML)-based models for in-ICU mortality for SP, providing guidance for preventive strategies.</p><p><strong>Methods: </strong>Retrospective data from adult SP patients at two hospitals (Yantaishan: training; Longkou: external validation; June 2023-Feb 2025) were analyzed. LASSO regression identified key predictors. Five ML models (Logistic Regression (LR) , Random Forest (RF), RBF-SVM, Linear SVM, (XGBoost) were built. The area under the ROC curve (AUC) was utilized to evaluate the overall model performance.Model performance (AUC, sensitivity, specificity at optimal threshold via Youden index), calibration, and clinical utility (decision curve) were evaluated on the external set.</p><p><strong>Results: </strong>In total, 501 patients were ultimately included, among whom 222 (44 %) died in the ICU. LASSO regression identified age, use of vasopressors, recent chemotherapy, SpO₂ within 8 h of ICU admission, D-dimer, platelet count, NT-proBNP, and use of invasive mechanical ventilation as modeling variables. In the external validation set, model performance was as follows: LR (AUC = 0.76; threshold = 0.339; sensitivity = 0.761; specificity = 0.639); RF (AUC = 0.77; threshold = 0.574; sensitivity = 0.448; specificity = 0.876); RBF-SVM (AUC = 0.746; threshold = 0.404; sensitivity = 0.642;specificity = 0.701); SVM-Linear (AUC = 0.741; threshold = 0.475; sensitivity = 0.507; specificity = 0.814); XGBoost (AUC = 0.76; threshold = 0.459; sensitivity = 0.597; specificity = 0.742). Based on the optimal threshold probability, LR exhibited the best clinical accuracy. Therefore, a predictive nomogram was developed using LR.</p><p><strong>Conclusion: </strong>ML models based on common interpretable clinical features demonstrate favorable predictive value for in-ICU mortality in SP patients, providing guidance for preventive strategies in clinical practice. However, the predictive performance requires further improvement. Therefore, future studies should incorporate additional efficient biomarkers to enhance model performance.</p>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"204 ","pages":"106075"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence generated visual communication improves comprehension and adherence in cervical cancer screening: a randomized controlled study","authors":"Kldiashvili Ekaterina ,&nbsp;Kaufmann Andreas Martin ,&nbsp;Khuntsaria Irakli ,&nbsp;Kekelia Elene ,&nbsp;Abuladze Mariam","doi":"10.1016/j.ijmedinf.2025.106134","DOIUrl":"10.1016/j.ijmedinf.2025.106134","url":null,"abstract":"<div><h3>Background</h3><div>Cervical cancer is preventable, yet poor comprehension of Pap smear results and non-adherence to follow-up major barriers, particularly in low health literacy settings. In Georgia, where screening coverage is below 20%, innovative communication strategies are needed. Artificial intelligence (AI) offers opportunities to strengthen patient communication through adaptive, emotionally expressive visual tools.</div></div><div><h3>Objective</h3><div>To evaluate whether AI-generated visual explanations, paired with simplified text, improve comprehension, satisfaction, and follow-up adherence after cervical cancer screening compared with conventional text reporting.</div></div><div><h3>Methods</h3><div>A randomized controlled trial enrolled 3,000 women aged 21–65 who underwent Pap smear testing between March and October 2024. Participants were randomized to three groups: Control (standard text), Text-only (enhanced plain-language text), and Intervention (AI-generated visuals plus text). Visuals were created with Craiyon, refined through expert and patient feedback, and aligned with Bethesda categories. Surveys assessed comprehension, satisfaction, and follow-up intent, while electronic records verified adherence. Analyses included chi-square tests, Kruskal-Wallis conformation for ordinal outcomes, and logistic regression for demographics and health literacy.</div></div><div><h3>Results</h3><div>The Intervention group achieved superior outcomes across all metrics. Comprehension reached 90 % versus 78 % in Text-only and 65 % in Control (χ²(2) = 131.8, p &lt; 0.001). Satisfaction was 90 % in the Intervention group, compared with 78 % and 65 %. Follow-up adherence was 75 % with AI visuals, 65 % with Text-only, and 50 % with Control, corresponding to a threefold increase in odds of adherence (OR = 3.0; 95 % CI: 2.5–3.6; H(2) = 136.3, p &lt; 0.001). Gains were most pronounced for abnormal results, including ASCUS, LSIL, and HSIL.</div></div><div><h3>Conclusions</h3><div>AI-generated visual communication significantly improved comprehension, satisfaction, and follow-up adherence in cervical cancer screening. This study demonstrates a scalable informatics solution for patient engagement, though challenges remain regarding long-term behavioral impact, cross-cultural adaptation, and integration into routine health information systems.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"205 ","pages":"Article 106134"},"PeriodicalIF":4.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable prediction self-assessment model for potentially inappropriate prescribing risk in older adults","authors":"Fangyuan Tian ,&nbsp;Zhaoyan Chen ,&nbsp;Mengnan Zhao ,&nbsp;Rui Tang ,&nbsp;Ying Zhang ,&nbsp;Qiyi Feng","doi":"10.1016/j.ijmedinf.2025.106137","DOIUrl":"10.1016/j.ijmedinf.2025.106137","url":null,"abstract":"<div><h3>Background</h3><div>Older adults with multiple chronic conditions often face challenges of potentially inappropriate prescribing (PIP). This study aimed to develop and validate an explainable machine learning (ML) model to predict PIP risk, aiding clinicians and patients in improving medication safety and self-management.</div></div><div><h3>Methods</h3><div>Data from geriatric outpatient prescriptions in six Chinese cities were analyzed using Chinese criteria. LASSO regression identified risk variables. Three machine learning (ML) models—logistic regression (LR), random forest (RF), and neural network (NN)—were training and internal validation (7: 3) and external validation cohort. Model performance was assessed via area under the ROC curve (AUC). SHapley Additive exPlanation (SHAP) values explained variable importance, and risk cutoff points were determined using the Youden index and prevalence data.</div></div><div><h3>Results</h3><div>Among 131,894 prescriptions, 29.00% (38,245) were PIP. The NN model with nonsampling performed best, with internal validation AUC of 0.759 (95%CI: 0.753 –0.764) and external validation AUC of 0.842 (95%CI: 0.816 –0.867). SHAP summary plots showed that the number of medications and sleep disorders were the most influential variables in basic prescription information and diagnoses, respectively. A predicted probability cutoff point of 29% was determined to classify low- and high-risk PIP categories. The optimal model was deployed as a web application (<span><span>https://stoppip.online/pipview</span><svg><path></path></svg></span>) for clinical use, and a WeChat mini-program was developed to facilitate self-assessment of PIP risk during outpatient follow-ups or home medication use.</div></div><div><h3>Conclusion</h3><div>The model was not only developed to predict PIP but can also be used by medical staff and older patients for self-assessment.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"205 ","pages":"Article 106137"},"PeriodicalIF":4.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twins in cardiovascular disease: a scoping review","authors":"Huina Zou ,&nbsp;Xinglin Zheng ,&nbsp;Linjing Wu ,&nbsp;Shujie Zhang ,&nbsp;Polun Chang ,&nbsp;Yuan Chen","doi":"10.1016/j.ijmedinf.2025.106138","DOIUrl":"10.1016/j.ijmedinf.2025.106138","url":null,"abstract":"<div><h3>Background</h3><div>Digital twin technology in healthcare is an emerging approach that creates virtual representations of patients and disease-specific conditions, with the potential to clarify treatment objectives and enable more personalized, precision-based care, to help to clarify treatment objectives and to facilitate personalised and precision treatment management.</div></div><div><h3>Objective</h3><div>This scoping review was conducted to analyse the application of digital twin technology in cardiovascular disease, focusing on implementation steps, clinical applications, and challenges to guide future research.</div></div><div><h3>Methods</h3><div>A systematic search was conducted in eight databases (PubMed, EBSCO, Web of Science, WILEY, China WanFang Database, China National Knowledge Infrastructure, China Weipu Database, and SinoMed) for studies published, with a time frame of database construction to May 2025. Data were summarised and analysed based on predefined criteria.</div></div><div><h3>Results</h3><div>A total of 31 cardiovascular studies were included. Their implementation was categorised into five stages: data acquisition, model construction and personalisation, model calibration and validation, simulation analysis, and result application for decision support or medical education. Clinical applications involved personalised health management (13 %), precise individual treatment effects (42 %), individual risk prediction (26 %), clinical trial optimisation (23 %), and medical education (3 %). Key challenges included data limitations, model construction and validation complexities, and barriers to clinical application.</div></div><div><h3>Conclusion</h3><div>Digital twins demonstrate potential in cardiovascular care by advancing personalised health management and precision medicine. However, their widespread adoption and practical implementation are still in their early stages. Broader implementation necessitates improved data sharing, algorithm optimisation, enhanced model generalizability, and ethical safeguards.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"206 ","pages":"Article 106138"},"PeriodicalIF":4.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions","authors":"David B. Olawade ,&nbsp;Sandra Chinaza Fidelis ,&nbsp;Sheila Marinze ,&nbsp;Eghosasere Egbon ,&nbsp;Ayodele Osunmakinde ,&nbsp;Augustus Osborne","doi":"10.1016/j.ijmedinf.2025.106141","DOIUrl":"10.1016/j.ijmedinf.2025.106141","url":null,"abstract":"<div><h3>Background</h3><div>Clinical trials face unprecedented challenges including recruitment delays affecting 80% of studies, escalating costs exceeding $200 billion annually in pharmaceutical R&amp;D, success rates below 12%, and data quality issues affecting 50% of datasets. Artificial intelligence (AI) offers transformative solutions to address these systemic inefficiencies across the clinical trial lifecycle.</div></div><div><h3>Objective</h3><div>To evaluate the current state, future potential, and implementation challenges of AI technologies in clinical trials, providing evidence-based guidance for responsible AI integration while maintaining patient safety and scientific integrity.</div></div><div><h3>Method</h3><div>Comprehensive narrative review following established guidelines for literature synthesis. Systematic search of PubMed, Embase, IEEE Xplore, and Google Scholar databases from January 2015 to December 2024. Data extraction and narrative synthesis organized thematically according to clinical trial lifecycle stages.</div></div><div><h3>Results</h3><div>Analysis of relevant studies demonstrated substantial AI benefits: patient recruitment tools improved enrollment rates by 65%, predictive analytics models achieved 85% accuracy in forecasting trial outcomes, and AI integration accelerated trial timelines by 30–50% while reducing costs by up to 40%. Digital biomarkers enabled continuous monitoring with 90% sensitivity for adverse event detection. However, significant implementation barriers emerged, including data interoperability challenges, regulatory uncertainty, algorithmic bias concerns, and limited stakeholder trust.</div></div><div><h3>Conclusion</h3><div>AI represents a transformative force in clinical research with proven capabilities to enhance efficiency, reduce costs, and improve patient outcomes. Realizing this potential requires addressing technical infrastructure limitations, developing explainable AI systems, establishing comprehensive regulatory frameworks, and fostering collaborative efforts between technology developers, clinical researchers, and regulatory agencies to ensure responsible implementation.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"206 ","pages":"Article 106141"},"PeriodicalIF":4.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the gap: Artificial intelligence knowledge, confidence, and education needs in clinical practice","authors":"Athanasios S. Naum,&nbsp;Robert S. Gordon,&nbsp;Anushka Deogaonkar,&nbsp;Marie L. Borum","doi":"10.1016/j.ijmedinf.2025.106132","DOIUrl":"10.1016/j.ijmedinf.2025.106132","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"205 ","pages":"Article 106132"},"PeriodicalIF":4.1,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI for colorectal cancer mortality and risk factor prediction in Korea: A nationwide cancer cohort study","authors":"Sang Won Park ,&nbsp;Na Young Yeo ,&nbsp;Tae-Hoon Kim ,&nbsp;Myoung Nam Lim ,&nbsp;Inhyeok Yim ,&nbsp;Oh Beom Kwon ,&nbsp;Seung-Joo Nam ,&nbsp;Hui-Young Lee ,&nbsp;Woo Jin Kim","doi":"10.1016/j.ijmedinf.2025.106125","DOIUrl":"10.1016/j.ijmedinf.2025.106125","url":null,"abstract":"<div><h3>Background</h3><div>Colorectal cancer (CRC) prognosis varies significantly, yet conventional statistical models struggle to capture the complex, non-linear interactions among clinical variables. Furthermore, most predictive models are based on Western populations, limiting their applicability to Korean patients. This study aimed to develop an explainable AI (XAI) model for CRC mortality prediction using a nationwide Korean cohort to provide clinically actionable insights.</div></div><div><h3>Methods</h3><div>We conducted a retrospective cohort study using the Korean Cancer Public Library Database. A total of 9,069 patients with CRC were analyzed for all-cause mortality (1,878 deaths) and 8,589 patients for CRC-specific mortality (1,398 deaths). Four ML algorithms—support vector machine, random forest, XGBoost, and LightGBM—were constructed. We employed explainable AI techniques, including SHapley Additive exPlanations (SHAP), to quantify the contribution of each predictor and ensure model interpretability.</div></div><div><h3>Results</h3><div>All models showed good discrimination (AUC: 0.82–0.94). LightGBM was presented as the best-optimized model with an AUC of 0.824 [95% CI 0.80–0.85] in all-cause mortality. For CRC-specific mortality, LGB again yielded the AUC of 0.867 [95% CI 0.84–0.89]. SHAP revealed tumor stage and carcinoembryonic antigen as top mortality predictors across ages. Metabolic markers (e.g., hypertension, cholesterol) and liver enzymes were more predictive in younger patients.</div></div><div><h3>Conclusions</h3><div>We developed the first interpretable machine learning model that accurately predicts CRC survival in a nationwide Korean cohort. Age-specific risk factors identified by SHAP not only support personalized care but also advance the application of precision oncology in Asian settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"205 ","pages":"Article 106125"},"PeriodicalIF":4.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using FHIR for data sharing: A scoping review of challenges and facilitators in healthcare settings","authors":"Raoof Nopour","doi":"10.1016/j.ijmedinf.2025.106128","DOIUrl":"10.1016/j.ijmedinf.2025.106128","url":null,"abstract":"<div><h3>Background and aim</h3><div>The sharing and integration of digital data are essential for optimizing the quality of clinical decision-making in healthcare. Fast Healthcare Interoperability Resources (FHIR) is a relatively novel standard in healthcare, playing a key role in enhancing the interoperability of healthcare information systems. However, using FHIR faces some challenges in the healthcare environment, hindering its optimal utilization. Identifying challenges and facilitators is crucial in enhancing its use and contributes to more efficient interoperability between health information systems (HISs). Therefore, this scoping review aims to identify these factors using the existing literature to provide a better understanding of this subject in healthcare settings.</div></div><div><h3>Materials and methods</h3><div>This scoping review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist. Four databases of the Web of Science (WoS), PubMed, Scopus, and Google Scholar were searched until 15 Apr 2025, without any time limitation, to retrieve relevant articles on this topic. The data on challenges and facilitators were obtained using qualitative content analysis. The results were presented using descriptive statistics, tables, and narrative synthesis.</div></div><div><h3>Results</h3><div>After searching the databases, applying the eligibility criteria, and conducting manual searches, eight articles were selected for data extraction and analysis. Seventy-three challenges and 43 facilitators related to the use of FHIR in healthcare were identified. The challenges were classified into organizational, technical, individual, data management, and legal, ethical, and regulatory. The facilitators were categorized into four categories: organizational, legal and regulatory, data management, and technical factors.</div></div><div><h3>Conclusion</h3><div>This scoping review highlights the challenges and facilitators that influence the adoption of FHIR in healthcare environments. Managers, policy-makers, designers of healthcare information systems, and other stakeholders can consider these factors to establish a guideline or roadmap to address critical barriers, such as technical and organizational challenges, to enhance the chance of FHIR adoption for data sharing between different HISs in healthcare settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"205 ","pages":"Article 106128"},"PeriodicalIF":4.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing readability and referencing in AI-generated patient education materials for spinal surgery","authors":"Mingbo Chen ,&nbsp;Fan Wang ,&nbsp;Dongfeng Pan ,&nbsp;Peifeng Liang","doi":"10.1016/j.ijmedinf.2025.106130","DOIUrl":"10.1016/j.ijmedinf.2025.106130","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"205 ","pages":"Article 106130"},"PeriodicalIF":4.1,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}