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Applied Sciences
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Large Language Models: Transforming E-health
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Large Language Models: Transforming E-health

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 28072

Special Issue Editors

Dr. Thomas Heston

E-Mail Website
Guest Editor
Department of Family Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
Interests: large language models; prompt engineering; statistical fragility; blockchain technology; responsible AI
Dr. Enno van der Velde

E-Mail Website
Guest Editor
Department of Cardiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
Interests: data standardization; medical technology; data exchange; e-health; biomedical informatics; privacy and security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advancements in large language models (LLMs) have opened up exciting new possibilities for transforming various domains, including e-health. LLMs, with their ability to understand, generate, and analyze human language at an unprecedented scale, are poised to revolutionize healthcare delivery, patient engagement, and medical research. The application of LLMs in e-health holds immense potential for improving patient outcomes, optimizing clinical processes, and driving innovation. From enhancing medical decision support systems to powering intelligent chatbots for patient interaction, LLMs are enabling a new era of smart, personalized, and accessible healthcare solutions.

This Special Issue explores the transformative impact of large language models on e-health. We invite contributions that showcase novel applications, cutting-edge research, and real-world case studies demonstrating the effectiveness of LLMs in addressing critical challenges and creating value in the e-health domain.

Topics of interest include, but are not limited to, the following:

  • Innovative applications of LLMs for medical text analysis, patient engagement, and clinical decision support;
  • Strategies for integrating LLMs into existing e-health systems and workflows;
  • Evaluation frameworks and metrics for assessing the performances and impacts of LLMs in e-health settings;
  • Ethical considerations, privacy concerns, and responsible development of LLMs for healthcare;
  • Future research directions and opportunities at the intersection of LLMs and e-health.

We encourage submissions from software engineers, researchers, practitioners, and industry experts at the forefront of LLM applications in e-health. By bringing together diverse perspectives and showcasing the latest advancements, this Special Issue aims to provide a comprehensive overview of LLMs' current state and future potential for transforming e-health.

Dr. Thomas Heston
Dr. Enno van der Velde
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • large language models
  • generative AI
  • responsible AI
  • clinical decision support
  • natural language processing
  • e-health

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Published Papers (3 papers)

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Research

31 pages, 1317 KiB  
Article
Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation
by Alper Karamanlıoğlu, Berkan Demirel, Onur Tural, Osman Tufan Doğan and Ferda Nur Alpaslan
Appl. Sci. 2025, 15(15), 8412; https://doi.org/10.3390/app15158412 - 29 Jul 2025
Viewed by 668
Abstract
This study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming analytics within a modular, privacy-preserving framework, the system addresses key deployment challenges in [...] Read more.
This study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming analytics within a modular, privacy-preserving framework, the system addresses key deployment challenges in high-stakes clinical settings. Unlike traditional models, the architecture processes both structured (vitals, labs) and unstructured (clinical notes) data to enable context-aware reasoning with clinically acceptable latency at the point of care. It leverages big data infrastructure for large-scale EHR management and incorporates digital twin concepts for live patient monitoring. Federated training allows institutions to collaboratively improve models without sharing raw data, ensuring compliance with GDPR/HIPAA, and FAIR principles. Privacy is further protected through differential privacy, secure aggregation, and inference isolation. We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (n = 132, 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. The system demonstrated clinically acceptable latency and promising alignment with expert judgment on reviewed cases. The infectious disease triage case demonstrates low-latency recognition of sepsis-like presentations in the ED. This work offers a scalable, audit-compliant, and clinician-validated blueprint for CDSS, enabling low-latency triage and extensibility across specialties. Full article
(This article belongs to the Special Issue Large Language Models: Transforming E-health)
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32 pages, 2628 KiB  
Article
JAVIS Chat: A Seamless Open-Source Multi-LLM/VLM Deployment System to Be Utilized in Single Computers and Hospital-Wide Systems with Real-Time User Feedback
by Javier Aguirre and Won Chul Cha
Appl. Sci. 2025, 15(4), 1796; https://doi.org/10.3390/app15041796 - 10 Feb 2025
Viewed by 2509
Abstract
The rapid advancement of large language models (LLMs) and vision-language models (VLMs) holds enormous promise across industries, including healthcare but hospitals face unique barriers, such as stringent privacy regulations, heterogeneous IT infrastructures, and limited customization. To address these challenges, we present the joint [...] Read more.
The rapid advancement of large language models (LLMs) and vision-language models (VLMs) holds enormous promise across industries, including healthcare but hospitals face unique barriers, such as stringent privacy regulations, heterogeneous IT infrastructures, and limited customization. To address these challenges, we present the joint AI versatile implementation system chat (JAVIS chat), an open-source framework for deploying LLMs and VLMs within secure hospital networks. JAVIS features a modular architecture, real-time feedback mechanisms, customizable components, and scalable containerized workflows. It integrates Ray for distributed computing and vLLM for optimized model inference, delivering smooth scaling from single workstations to hospital-wide systems. JAVIS consistently demonstrates robust scalability and significantly reduces response times on legacy servers through Ray-managed multiple-instance models, operating seamlessly across diverse hardware configurations and enabling real-time departmental customization. By ensuring compliance with global data protection laws and operating solely within closed networks, JAVIS safeguards patient data while facilitating AI adoption in clinical workflows. This paradigm shift supports patient care and operational efficiency by bridging AI potential with clinical utility, with future developments including speech-to-text integration, further enhancing its versatility. Full article
(This article belongs to the Special Issue Large Language Models: Transforming E-health)
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30 pages, 2287 KiB  
Article
Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach
by Vito Santamato, Caterina Tricase, Nicola Faccilongo, Massimo Iacoviello and Agostino Marengo
Appl. Sci. 2024, 14(22), 10144; https://doi.org/10.3390/app142210144 - 6 Nov 2024
Cited by 21 | Viewed by 23704
Abstract
The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic [...] Read more.
The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts. Utilizing an integrated approach, we compared various prediction algorithms, including logistic regression, and interpreted the results through SHAP (SHapley Additive exPlanations) analysis. The findings reveal five key thematic areas: AI’s role in enhancing quality assurance, resource management, technological innovation, security, and the healthcare response to the COVID-19 pandemic. The study highlights AI’s positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration. These insights provide opportunities for targeted interventions to optimize AI’s impact in current and future healthcare landscapes. In conclusion, this work contributes to a deeper understanding of the role of AI in healthcare management and provides insights for policymakers, healthcare professionals, and researchers, offering a roadmap for addressing both the opportunities and challenges posed by AI integration in the healthcare sector. Full article
(This article belongs to the Special Issue Large Language Models: Transforming E-health)
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