PLOS digital health最新文献

{"title":"Retrieval augmented generation for large language models in healthcare: A systematic review.","authors":"Lameck Mbangula Amugongo, Pietro Mascheroni, Steven Brooks, Stefan Doering, Jan Seidel","doi":"10.1371/journal.pdig.0000877","DOIUrl":"10.1371/journal.pdig.0000877","url":null,"abstract":"<p><p>Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate (\"hallucinated\") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based approaches employed by LLMs in healthcare, focusing on the different steps of retrieval, augmentation and generation. Additionally, we identify the limitations, strengths and gaps in the existing literature. Our synthesis shows that 78.9% of studies used English datasets and 21.1% of the datasets are in Chinese. We find that a range of techniques are employed RAG-based LLMs in healthcare, including Naive RAG, Advanced RAG, and Modular RAG. Surprisingly, proprietary models such as GPT-3.5/4 are the most used for RAG applications in healthcare. We find that there is a lack of standardised evaluation frameworks for RAG-based applications. In addition, the majority of the studies do not assess or address ethical considerations related to RAG in healthcare. It is important to account for ethical challenges that are inherent when AI systems are implemented in the clinical setting. Lastly, we highlight the need for further research and development to ensure responsible and effective adoption of RAG in the medical domain.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000877"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276902","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":"Exploring the sustainability of virtual care interventions: A scoping review.","authors":"Tujuanna Austin, Farah Tahsin, Darren Larsen, Ross Baker, Carolyn Steele Gray","doi":"10.1371/journal.pdig.0000893","DOIUrl":"10.1371/journal.pdig.0000893","url":null,"abstract":"<p><p>During the COVID-19 pandemic, virtual care has proven instrumental in ensuring the continuity of healthcare services. In the context of virtual care's growing prominence and continued use, understanding how and why virtual care interventions are sustained will help healthcare systems to better prepare for future crises. The objectives of this scoping review were to construct a conceptualization of of virtual care sustainability and to describe factors influencing the sustainability of virtual care, shedding light on the determinants that shape its longevity and continued use. Literature describing the sustainability of virtual care interventions was summarized. Details of the intervention, setting, methodology, description and evidence of sustainability, and synopsis of key findings were documented. The charted data were summarized to gain a descriptive understanding of the data collected and to establish patterns. A conceptualization of virtual care intervention sustainability focused on the concepts of fidelity and adaptability. Sustainability of virtual care interventions were conceptualized as the intervention's ability to continue to be used according to its initial design, the extent to which the intervention continued to achieve its intended outcomes (fidelity), and the ability of the intervention to evolve as the context in which it is used also evolves (adaptability). While there were various definitions of sustainability referenced, no included studies mentioned a definition of sustainability specific to virtual care. Commonalities in definitions included the continued use of virtual care and the continuation of the benefits of virtual care for some period of time. Findings indicate that there is no \"one size fits all\" approach to achieving sustainability of virtual care interventions, but instead identify factors that may support or hinder sustainability. Important to understanding sustainability of virtual care interventions, is the complexity of the interactions that influence it. Specifically, the factors of fidelity and adaptability are found to be important to understanding the sustainability of virtual care interventions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000893"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12157087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276900","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":"A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach.","authors":"Navreet Kaur, Manuel Gonzales Iv, Cristian Garcia Alcaraz, Jiaqi Gong, Kristen J Wells, Laura E Barnes","doi":"10.1371/journal.pdig.0000839","DOIUrl":"10.1371/journal.pdig.0000839","url":null,"abstract":"<p><p>Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision - 92.04%, Recall - 93.15%, Specificity - 77.50%), and weekly models, an accuracy of 76.04% (Precision - 75.83%, Recall - 85.80%, Specificity - 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more comprehensive assessment of medication adherence. Nonetheless, we assessed a theory-informed, multi-scale approach to predict adherence, and our findings offer valuable insights to guide the designing of personalized, technology-driven adherence interventions and fostering collaboration among patients, healthcare providers, and caregivers to support long-term adherence.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000839"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268057","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":"Performance of a smartphone-based malaria screener in detecting malaria in people living with Sickle cell disease.","authors":"Deborah Nimako Sarpong Obeng, Samuel Osei, Nii Kpakpo Brown, David Nana Adjei, Linda Eva Amoah, Ewurama Dedea Ampadu Owusu","doi":"10.1371/journal.pdig.0000884","DOIUrl":"10.1371/journal.pdig.0000884","url":null,"abstract":"<p><p>Novel automated digital malaria diagnostic tests are being developed with the advancement of diagnostic tools. Whilst these tools are being evaluated and implemented in the general population, there is the need to focus on special populations such as individuals with Sickle Cell Disease (SCD) who have altered red blood cell morphology and atypical immune responses, which can obscure parasite detection. This study aimed to evaluate the diagnostic performance of one of such tools, the National Library of Medicine (NLM) malaria screener app in people living with sickle cell disease in a malaria-endemic country, Ghana. A descriptive cross-sectional study was conducted among SCD patients attending the Sickle Cell Clinic at Korle Bu Teaching Hospital in Accra, Ghana. Following informed consent, whole blood samples were collected and analyzed using the NLM malaria screener app, conventional microscopy, RDT, and Polymerase Chain Reaction (PCR), with PCR as the reference standard. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each diagnostic method were compared against PCR results. The NLM app identified the highest number of positive malaria cases, with 110 positive cases (36.2%), while both RDT and microscopy reported the highest number of negatives, with 287 negative cases (94.4%). Compared to PCR, the NLM app demonstrated a sensitivity of 89.5% and a specificity of 67.4%. RDT and microscopy displayed the same sensitivity as the NLM app, each achieving 89.5%. However, while RDT and microscopy had a specificity of 100%, the NLM app had a considerably lower specificity of 67.4%.The NLM malaria screener app shows promise as a preliminary screening tool for malaria in individuals with SCD. However, its lower specificity indicates a need for confirmatory testing to avoid potential overdiagnosis and mismanagement. Enhancements in the app's specificity could further support its utility in rapid and accessible malaria diagnosis for people with SCD, aiding in timely management and treatment.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000884"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12148164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259522","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 illusion of safety: A report to the FDA on AI healthcare product approvals.","authors":"Rawan Abulibdeh, Leo Anthony Celi, Ervin Sejdić","doi":"10.1371/journal.pdig.0000866","DOIUrl":"10.1371/journal.pdig.0000866","url":null,"abstract":"<p><p>Artificial intelligence is rapidly transforming healthcare, offering promising advancements in diagnosis, treatment, and patient outcomes. However, concerns regarding the regulatory oversight of artificial intelligence driven medical technologies have emerged, particularly with the U.S. Food and Drug Administration's current approval processes. This paper critically examines the U.S. Food and Drug Administration's regulatory framework for artificial intelligence powered healthcare products, highlighting gaps in safety evaluations, post-market surveillance, and ethical considerations. Artificial intelligence's continuous learning capabilities introduce unique risks, as algorithms evolve beyond their initial validation, potentially leading to performance degradation and biased outcomes. Although the U.S. Food and Drug Administration has taken steps to address these challenges, such as artificial intelligence/machine learning-based software as a medical device action plan and proposed regulatory adjustments, significant weaknesses remain, particularly in real-time monitoring, transparency and bias mitigation. This paper argues for a more adaptive, community-engaged regulatory approach that mandates extensive post-market evaluations, requires artificial intelligence developers to disclose training data sources, and establishes enforceable standards for fairness, equity, and accountability. A patient-centered regulatory framework must also integrate diverse perspectives to ensure artificial intelligence technologies serve all populations equitably. By fostering an agile, transparent, and ethics-driven oversight system, the U.S. Food and Drug Administration can balance innovation with patient safety, ensuring that artificial intelligence-driven medical technologies enhance, rather than compromise, healthcare outcomes.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000866"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236162","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":"Pharmacist-led rapid uptitration clinic in heart failure patients with reduced ejection fraction: Our experience within a virtual ward.","authors":"Hussein Alhakem, Angela Murphy, Liuba Fusco, Grant McQueen, Sarah Pearse, Jodian Barrett, Deirdre Linnard, Sadia Khan","doi":"10.1371/journal.pdig.0000868","DOIUrl":"10.1371/journal.pdig.0000868","url":null,"abstract":"<p><p>Heart failure with reduced ejection fraction is a chronic, progressive medical condition affecting millions of individuals worldwide. It is associated with high morbidity and mortality. The use of \"foundational quadruple therapy\" titrated to the maximum tolerated doses improves survival, quality of life, and reduces heart failure-related hospitalisation. Despite this evidence, there is a consistent trend of suboptimal dose up-titration, prolonged optimisation periods, and early therapy discontinuation. Virtual wards offer a potential innovative solution in transforming heart failure management by combining rapid medication optimisation with remote monitoring to improve patient outcomes. This retrospective study employed a single-group pre-post design to evaluate the effectiveness of a prescribing pharmacist in the rapid uptitration of Guidelines Directed Medical Therapy (GDMT) in patients with heart failure with reduced ejection fraction within a virtual ward setting. The study assessed clinical outcomes of 86 patients at baseline, following discharge from the virtual ward (typically after 4 weeks), and at 3-6 months post-discharge. Improvements were seen in NYHA scores, cardiac systolic function, and Optimal Medical Therapy (OMT) scores. The median Left Ventricular Ejection Fraction increased from 29% at baseline to 39% post-optimisation, signifying improved myocardial performance and a reduction in the severity of left ventricular dysfunction. Post-optimisation, 37% of patients attained an optimal OMT score of 8, 52% attained an acceptable score (5-7), and only 5% remained in the suboptimal range (0-4). Additionally, 84% of patients were prescribed all four foundational therapies. There was no notable increase in adverse events such as hypotension, bradycardia, or hyperkalaemia. Remote up-titration of heart failure medications within a virtual ward environment is a promising approach, offering a fast, feasible, safe, and efficient treatment solution for patients who are otherwise undertreated.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000868"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236160","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":"Transforming social media text into predictive tools for depression through AI: A test-case study on the Beck Depression Inventory-II.","authors":"Federico Ravenda, Antonio Preti, Michele Poletti, Antonietta Mira, Fabio Crestani, Andrea Raballo","doi":"10.1371/journal.pdig.0000848","DOIUrl":"10.1371/journal.pdig.0000848","url":null,"abstract":"<p><p>The characterization of mental states through assessment tools is a fundamental aspect in psychiatric and psychological clinical practice. In this context, standardized questionnaires based on Likert scales are often used for the assessment of emotions, attitudes, and perceptions. These tools enable clinicians and researchers to quantify subjective experiences, providing valuable data that elucidate the intricate nature of human emotions and beliefs. Despite their utility, administering and completing these questionnaires presents significant challenges. The process requires substantial time and resources from both clinicians and participants, which can create barriers to efficient data collection and analysis. Consequently, we aim to streamline this process without compromising the quality and reliability of the gathered data. This study was designed to develop a tool (aka EnsemBERT) that leveraging the power of Pre-trained Language Models (PLMs) could reliably predict the scores associated with each item of the Beck Depression Inventory (BDI-II) on the basis of users' generated social media posts. The results confirm that such AI-based approach is feasible and that the specific tool, i.e. EnsemBERT, can accurately predict questionnaire scores at various levels of granularity, i.e. individual item scores as well as overall questionnaire scores.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000848"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236163","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":"Young people's data protection and privacy rights must not be neglected in the digital transformation of health: Insights, perspectives, and recommendations for the African context.","authors":"Victor Oluwafemi Femi-Lawal, Temilola Aderemi","doi":"10.1371/journal.pdig.0000872","DOIUrl":"10.1371/journal.pdig.0000872","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000872"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236165","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 digital crossroads: Media literacy and the future of youth online.","authors":"Kate Barranco, Kendall Bryant","doi":"10.1371/journal.pdig.0000876","DOIUrl":"10.1371/journal.pdig.0000876","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000876"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236161","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":"When evidence is not enough: A qualitative exploration of healthcare workers' perspectives on expansion of two-way texting (2wT) for post- circumcision follow-up in South Africa.","authors":"Isabella Fabens, Calsile Makhele, Nelson Igaba, Khumbulani Moyo, Felex Ndebele, Jacqueline Pienaar, Geoffrey Setswe, Caryl Feldacker","doi":"10.1371/journal.pdig.0000867","DOIUrl":"10.1371/journal.pdig.0000867","url":null,"abstract":"<p><p>As per South African national guidelines, in-person follow-up visits after voluntary medical male circumcision (VMMC) are required but may be unnecessary. Two-way texting (2wT), an mHealth platform, engages clients in post-operative care and triages those with complications to in-person review. 2wT was found to be safe, effective, and efficient. In South Africa, to understand provider perspectives on 2wT and potential for expansion, 20 key informant interviews were conducted with management, clinicians, data officials and support staff involved in 2wT scale-up. Interviews were analyzed using rapid qualitative methods and informed by two implementation science frameworks: the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework and the Pragmatic, Robust, Implementation and Sustainability Model (PRISM). Participants shared mixed and multi-faceted feedback, including that 2wT improves monitoring and evaluation of clients and clinical outcomes while also reducing follow-up visits. Challenges included duplicative routine and 2wT reporting systems and perceptions that 2wT increased workload. To improve the likelihood of successful 2wT scale-up in routine VMMC settings, participants suggested: further 2wT sensitization to ensure clinician and support staff buy-in; a dedicated clinician or nurse to manage telehealth clients; improved dashboards to better visualize 2wT client data; mobilizing 2wT champions at facilities to garner support for 2wT as routine care; and updating VMMC guidelines to support VMMC telehealth. As attendance at follow-up visits may not be as high as reported, implementing 2wT may require more effort but also brings added benefits of client verification and documented follow-up. The transition from research to routine practice is challenging, but use of RE-AIM and PRISM indicate that it is not impossible. As VMMC funding is decreasing, more effort to share the evidence base for 2wT as a safe, cost-effective, high-quality approach for VMMC follow-up is needed to encourage widespread uptake and adoption.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000867"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236164","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}