PLOS digital health最新文献

{"title":"Safety of human-AI cooperative decision-making within intensive care: A physical simulation study.","authors":"Paul Festor, Myura Nagendran, Anthony C Gordon, Aldo A Faisal, Matthieu Komorowski","doi":"10.1371/journal.pdig.0000726","DOIUrl":"10.1371/journal.pdig.0000726","url":null,"abstract":"<p><p>The safety of Artificial Intelligence (AI) systems is as much one of human decision-making as a technological question. In AI-driven decision support systems, particularly in high-stakes settings such as healthcare, ensuring the safety of human-AI interactions is paramount, given the potential risks of following erroneous AI recommendations. To explore this question, we ran a safety-focused clinician-AI interaction study in a physical simulation suite. Physicians were placed in a simulated intensive care ward, with a human nurse (played by an experimenter), an ICU data chart, a high-fidelity patient mannequin and an AI recommender system on a display. Clinicians were asked to prescribe two drugs for the simulated patients suffering from sepsis and wore eye-tracking glasses to allow us to assess where their gaze was directed. We recorded clinician treatment plans before and after they saw the AI treatment recommendations, which could be either 'safe' or 'unsafe'. 92% of clinicians rejected unsafe AI recommendations vs 29% of safe ones. Physicians paid increased attention (+37% gaze fixations) to unsafe AI recommendations vs safe ones. However, visual attention on AI explanations was not greater in unsafe scenarios. Similarly, clinical information (patient monitor, patient chart) did not receive more attention after an unsafe versus safe AI reveal suggesting that the physicians did not look back to these sources of information to investigate why the AI suggestion might be unsafe. Physicians were only successfully persuaded to change their dose by scripted comments from the bedside nurse 5% of the time. Our study emphasises the importance of human oversight in safety-critical AI and the value of evaluating human-AI systems in high-fidelity settings that more closely resemble real world practice.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000726"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11849858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495064","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":"Artificial intelligence as a tool for improving health literacy in kidney care.","authors":"Jing Miao, Charat Thongprayoon, Kianoush B Kashani, Wisit Cheungpasitporn","doi":"10.1371/journal.pdig.0000746","DOIUrl":"10.1371/journal.pdig.0000746","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000746"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473379","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":"Chatbot -assisted self-assessment (CASA): Co-designing an AI -powered behaviour change intervention for ethnic minorities.","authors":"Tom Nadarzynski, Nicky Knights, Deborah Husbands, Cynthia Graham, Carrie D Llewellyn, Tom Buchanan, Ian Montgomery, Alejandra Soruco Rodriguez, Chimeremumma Ogueri, Nidhi Singh, Evan Rouse, Olabisi Oyebode, Ankit Das, Grace Paydon, Gurpreet Lall, Anathoth Bulukungu, Nur Yanyali, Alexandra Stefan, Damien Ridge","doi":"10.1371/journal.pdig.0000724","DOIUrl":"10.1371/journal.pdig.0000724","url":null,"abstract":"<p><strong>Background: </strong>The digitalisation of healthcare has provided new ways to address disparities in sexual health outcomes that particularly affect ethnic and sexual minorities. Conversational artificial intelligence (AI) chatbots can provide personalised health education and refer users for appropriate medical consultations. We aimed to explore design principles of a chatbot-assisted culturally sensitive self-assessment intervention based on the disclosure of health-related information.</p><p><strong>Methods: </strong>In 2022, an online survey was conducted among an ethnically diverse UK sample (N = 1,287) to identify the level and type of health-related information disclosure to sexual health chatbots, and reactions to chatbots' risk appraisal. Follow-up interviews (N = 41) further explored perceptions of chatbot-led health assessment to identify aspects related to acceptability and utilisation. Datasets were analysed using one-way ANOVAs, linear regression, and thematic analysis.</p><p><strong>Results: </strong>Participants had neutral-to-positive attitudes towards chatbots and were comfortable disclosing demographic and sensitive health information. Chatbot awareness, previous experience and positive attitudes towards chatbots predicted information disclosure. Qualitatively, four main themes were identified: \"Chatbot as an artificial health advisor\", \"Disclosing information to a chatbot\", \"Ways to facilitate trust and disclosure\", and \"Acting on self-assessment\".</p><p><strong>Conclusion: </strong>Chatbots were acceptable for health self-assessment among this sample of ethnically diverse individuals. Most users reported being comfortable disclosing sensitive and personal information, but user anonymity is key to engagement with chatbots. As this technology becomes more advanced and widely available, chatbots could potentially become supplementary tools for health education and screening eligibility assessment. Future research is needed to establish their impact on screening uptake and access to health services among minoritised communities.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000724"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11824973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416509","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":"Diabetes-focused food recommender system (DFRS) to enabling digital health.","authors":"Esmael Ahmed, Mohammed Oumer, Medina Hassan","doi":"10.1371/journal.pdig.0000530","DOIUrl":"10.1371/journal.pdig.0000530","url":null,"abstract":"<p><p>The integration of digital health technologies into diabetes management has shown the potential to improve patient outcomes by providing personalized dietary recommendations. This study aims to develop and evaluate the Diabetes-Focused Food Recommender System (DFRS), a system designed to assist individuals with diabetes in making informed food choices. Using a combination of advanced machine learning algorithms, nutrition science, and digital health technologies, DFRS generates personalized recommendations tailored to individual needs. The methodology involves data collection from diverse patient profiles and model development using Graph Neural Networks (GNN) and other machine learning techniques. Hyperparameter tuning and rigorous performance evaluation were conducted to optimize system accuracy. The results demonstrate that after optimization, GNN achieved an accuracy of 94 percent, significantly enhancing the precision of dietary recommendations. Clinical validation of the system showed a reduction in HbA1c levels, glycemic variability, and incidents of hyper- and hypoglycemia. Therefore, DFRS has proven to be an effective tool for improving dietary management in diabetes care, and its integration into clinical workflows offers the potential to enhance health outcomes and streamline healthcare delivery.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000530"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411921","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":"The Neuropathy Tracker-A mobile health application for ambulatory and self-administred assessment of neuropathy.","authors":"Jakob E Bardram, Mads Westermann, Julia G Makulec, Martin Ballegaard","doi":"10.1371/journal.pdig.0000725","DOIUrl":"10.1371/journal.pdig.0000725","url":null,"abstract":"<p><p>Peripheral neuropathy is a common neurological disease and is a common complication of diabetes or cancer treatment. Early detection and treatment are crucial for improving the treatment of e.g., diabetic foot ulcers. However, neuropathy detection and monitoring requires examination of the motor and sensory systems and needs to be carried out in a clinical setting by trained professionals, leading to waiting time and delayed treatment. This paper presents the Neuropathy Tracker which is a Mobile Health (mHealth) tool for ambulatory self-assessment of neuropathy, which can be done by the patient at home. The app was designed in an iterative user-centered design (UCD) process involving neurologists, patients, and healthy subjects, thereby ensuring a high degree of clinical validity and usability. The assessment methodology in the app applies state-of-the-art neuropathy assessment methods and the app embodies a user-friendly and systematic assessment flow that guides the patient through the self-assessment. The Neuropathy Tracker tool was subject to a small feasibility study (N = 17), which showed a statistically significant (Pearson correlation ρ = 0.86, p < 0.05) but moderate (Concordance Correlation Coefficient (ρc) = 0.69) concurrent validity when compared with a standard clinical assessment method. All patients were able to complete the self-assessment without any help. As such, the technical and user experience design of the Neuropathy Tracker presents a stable mHealth tool that may be feasible for ambulatory self-assessment of neuropathy. Further clinical validation studies are, however, warranted before it is used in the clinical treatment of neuropathy.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000725"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412010","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":"Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings.","authors":"Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones","doi":"10.1371/journal.pdig.0000491","DOIUrl":"10.1371/journal.pdig.0000491","url":null,"abstract":"<p><strong>Objectives: </strong>Approximately 80% of people with epilepsy live in low- and middle-income countries (LMICs), where limited resources and stigma hinder accurate diagnosis and treatment. Clinical machine learning models have demonstrated substantial promise in supporting the diagnostic process in LMICs by aiding in preliminary screening and detection of possible epilepsy cases without relying on specialised or trained personnel. How well these models generalise to naïve regions is, however, underexplored. Here, we use a novel approach to assess the suitability and applicability of such clinical tools to aid screening and diagnosis of active convulsive epilepsy in settings beyond their original training contexts.</p><p><strong>Methods: </strong>We sourced data from the Study of Epidemiology of Epilepsy in Demographic Sites dataset, which includes demographic information and clinical variables related to diagnosing epilepsy across five sub-Saharan African sites. For each site, we developed a region-specific (single-site) predictive model for epilepsy and assessed its performance at other sites. We then iteratively added sites to a multi-site model and evaluated model performance on the omitted regions. Model performances and parameters were then compared across every permutation of sites. We used a leave-one-site-out cross-validation analysis to assess the impact of incorporating individual site data in the model.</p><p><strong>Results: </strong>Single-site clinical models performed well within their own regions, but generally worse when evaluated in other regions (p<0.05). Model weights and optimal thresholds varied markedly across sites. When the models were trained using data from an increasing number of sites, mean internal performance decreased while external performance improved.</p><p><strong>Conclusions: </strong>Clinical models for epilepsy diagnosis in LMICs demonstrate characteristic traits of ML models, such as limited generalisability and a trade-off between internal and external performance. The relationship between predictors and model outcomes also varies across sites, suggesting the need to update specific model aspects with local data before broader implementation. Variations are likely to be particular to the cultural context of diagnosis. We recommend developing models adapted to the cultures and contexts of their intended deployment and caution against deploying region- and culture-naïve models without thorough prior evaluation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000491"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412003","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":"Linguistic changes in spontaneous speech for detecting Parkinson's disease using large language models.","authors":"Jonathan L Crawford","doi":"10.1371/journal.pdig.0000757","DOIUrl":"10.1371/journal.pdig.0000757","url":null,"abstract":"<p><p>Parkinson's disease is the second most prevalent neurodegenerative disorder with over ten million active cases worldwide and one million new diagnoses per year. Detecting and subsequently diagnosing the disease is challenging because of symptom heterogeneity with respect to complexity, as well as the type and timing of phenotypic manifestations. Typically, language impairment can present in the prodromal phase and precede motor symptoms suggesting that a linguistic-based approach could serve as a diagnostic method for incipient Parkinson's disease. Additionally, improved linguistic models may enhance other approaches through fusion techniques. The field of large language models is advancing rapidly, presenting the opportunity to explore the use of these new models for detecting Parkinson's disease and to improve on current linguistic approaches with high-dimensional representations of linguistics. We evaluate the application of state-of-the-art large language models to detect Parkinson's disease automatically from spontaneous speech with up to 78% accuracy. We also demonstrate that large language models can be used to predict the severity of PD in a regression task. We further demonstrate that the better performance of large language models is due to their ability to extract more relevant linguistic features and not due to increased dimensionality of the feature space.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000757"},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11809853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392624","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":"Forecasting mental states in schizophrenia using digital phenotyping data.","authors":"Thierry Jean, Rose Guay Hottin, Pierre Orban","doi":"10.1371/journal.pdig.0000734","DOIUrl":"10.1371/journal.pdig.0000734","url":null,"abstract":"<p><p>The promise of machine learning successfully exploiting digital phenotyping data to forecast mental states in psychiatric populations could greatly improve clinical practice. Previous research focused on binary classification and continuous regression, disregarding the often ordinal nature of prediction targets derived from clinical rating scales. In addition, mental health ratings typically show important class imbalance or skewness that need to be accounted for when evaluating predictive performance. Besides it remains unclear which machine learning algorithm is best suited for forecast tasks, the eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) algorithms being 2  popular choices in digital phenotyping studies. The CrossCheck dataset includes 6,364 mental state surveys using 4-point ordinal rating scales and 23,551 days of smartphone sensor data contributed by patients with schizophrenia. We trained 120 machine learning models to forecast 10 mental states (e.g., Calm, Depressed, Seeing things) from passive sensor data on 2 predictive tasks (ordinal regression, binary classification) with 2 learning algorithms (XGBoost, LSTM) over 3 forecast horizons (same day, next day, next week). A majority of ordinal regression and binary classification models performed significantly above baseline, with macro-averaged mean absolute error values between 1.19 and 0.77, and balanced accuracy between 58% and 73%, which corresponds to similar levels of performance when these metrics are scaled. Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. In conclusion, when using performance metrics that properly account for class imbalance, ordinal forecast models demonstrated comparable performance to the prevalent binary classification approach without losing valuable clinical information from self-reports, thus providing richer and easier to interpret predictions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000734"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11805420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371357","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":"Community perspectives regarding brain-computer interfaces: A cross-sectional study of community-dwelling adults in the UK.","authors":"Austen El-Osta, Mahmoud Al Ammouri, Shujhat Khan, Sami Altalib, Manisha Karki, Eva Riboli-Sasco, Azeem Majeed","doi":"10.1371/journal.pdig.0000524","DOIUrl":"10.1371/journal.pdig.0000524","url":null,"abstract":"<p><strong>Background: </strong>Brain-computer interfaces (BCIs) represent a ground-breaking advancement in neuroscience, facilitating direct communication between the brain and external devices. This technology has the potential to significantly improve the lives of individuals with neurological disorders by providing innovative solutions for rehabilitation, communication and personal autonomy. However, despite the rapid progress in BCI technology and social media discussions around Neuralink, public perceptions and ethical considerations concerning BCIs-particularly within community settings in the UK-have not been thoroughly investigated.</p><p><strong>Objective: </strong>The primary aim of this study was to investigate public knowledge, attitudes and perceptions regarding BCIs including ethical considerations. The study also explored whether demographic factors were related to beliefs about BCIs increasing inequalities, support for strict regulations, and perceptions of appropriate fields for BCI design, testing and utilization in healthcare.</p><p><strong>Methods: </strong>This cross-sectional study was conducted between 1 December 2023 and 8 March 2024. The survey included 29 structured questions covering demographics, awareness of BCIs, ethical considerations and willingness to use BCIs for various applications. The survey was distributed via the Imperial College Qualtrics platform. Participants were recruited primarily through Prolific Academic's panel and personal networks. Data analysis involved summarizing responses using frequencies and percentages, with chi-squared tests to compare groups. All data were securely stored and pseudo-anonymized to ensure confidentiality.</p><p><strong>Results: </strong>Of the 950 invited respondents, 846 participated and 806 completed the survey. The demographic profile was diverse, with most respondents aged 36-45 years (26%) balanced in gender (52% female), and predominantly identifying as White (86%). Most respondents (98%) had never used BCIs, and 65% were unaware of them prior to the survey. Preferences for BCI types varied by condition. Ethical concerns were prevalent, particularly regarding implantation risks (98%) and costs (92%). Significant associations were observed between demographic variables and perceptions of BCIs regarding inequalities, regulation and their application in healthcare. Conclusion: Despite strong interest in BCIs, particularly for medical applications, ethical concerns, safety and privacy issues remain significant highlighting the need for clear regulatory frameworks and ethical guidelines, as well as educational initiatives to improve public understanding and trust. Promoting public discourse and involving stakeholders including potential users, ethicists and technologists in the design process through co-design principles can help align technological development with public concerns whilst also helping developers to proactively address ethical dilemmas.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000524"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257425","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":"From print to perspective: A mixed-method analysis of the convergence and divergence of COVID-19 topics in newspapers and interviews.","authors":"Qingqing Chen, Andrew Crooks, Adam J Sullivan, Jennifer A Surtees, Laurene Tumiel-Berhalter","doi":"10.1371/journal.pdig.0000736","DOIUrl":"10.1371/journal.pdig.0000736","url":null,"abstract":"<p><p>In the face of the unprecedented COVID-19 pandemic, various government-led initiatives and individual actions (e.g., lockdowns, social distancing, and masking) have resulted in diverse pandemic experiences. This study aims to explore these varied experiences to inform more proactive responses for future public health crises. Employing a novel \"big-thick\" data approach, we analyze and compare key pandemic-related topics that have been disseminated to the public through newspapers with those collected from the public via interviews. Specifically, we utilized 82,533 U.S. newspaper articles from January 2020 to December 2021 and supplemented this \"big\" dataset with \"thick\" data from interviews and focus groups for topic modeling. Identified key topics were contextualized, compared and visualized at different scales to reveal areas of convergence and divergence. We found seven key topics from the \"big\" newspaper dataset, providing a macro-level view that covers public health, policies and economics. Conversely, three divergent topics were derived from the \"thick\" interview data, offering a micro-level view that focuses more on individuals' experiences, emotions and concerns. A notable finding is the public's concern about the reliability of news information, suggesting the need for further investigation on the impacts of mass media in shaping the public's perception and behavior. Overall, by exploring the convergence and divergence in identified topics, our study offers new insights into the complex impacts of the pandemic and enhances our understanding of key issues both disseminated to and resonating with the public, paving the way for further health communication and policy-making.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000736"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257426","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}