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Artificial Intelligence Applications in Medical Data Analysis and Healthcare Virtual Assistants

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

Deadline for manuscript submissions: 20 August 2025 | Viewed by 2293

Special Issue Editor

Dr. Jordi Cusido

E-Mail Website
Guest Editor
Departament de Projectes i Construcció, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: interoperable electronic health repositories; healthcare metaverse; artificial intelligence as medical devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is intended to be a forum for sharing the latest advancements and exploratory research in the integration of Artificial Intelligence (AI) within the medical and healthcare domains. We seek innovative research that leverages AI, Natural Language Processing (NLP), Large Language Models (LLMs), and comprehensive data architectures to foster the development of medical devices and virtual assistants in healthcare. Contributions are encouraged in areas such as data engineering and data science that underscore the enhancement of healthcare outcomes. Through this confluence of technologies and methodologies, our objective is to spotlight groundbreaking approaches and technologies that pave the way for a transformative impact on patient care, medical procedures, and the overall healthcare ecosystem.

Dr. Jordi Cusido
Guest Editor

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

  • artificial intelligence
  • NLP
  • LLM
  • cloud computing
  • virtual environments
  • data architecture
  • data engineering

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Published Papers (2 papers)

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Research

17 pages, 2116 KiB  
Article
A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes
by Qisthi Alhazmi Hidayaturrohman and Eisuke Hanada
Appl. Sci. 2025, 15(6), 3393; https://doi.org/10.3390/app15063393 - 20 Mar 2025
Viewed by 335
Abstract
This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support [...] Read more.
This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The models were built using real patient data from the Zigong Fourth People’s Hospital, which included 167 features from 2008 patients. The mean, MICE, kNN, and RF imputation techniques were implemented to handle missing values. Our initial results showed that SVM models outperformed the others, achieving an accuracy of up to 0.6294, sensitivity above 0.61, and an AUC score exceeding 0.66. However, after 10-fold cross-validation, the RF models demonstrated superior robustness, with an average AUC improvement of 0.03815, whereas the SVM models showed potential for overfitting, with a slight decline (−0.0074). The XGBoost models exhibited moderate improvement (+0.01683) post-validation. Bayesian Search had the best computational efficiency, consistently requiring less processing time than the Grid and Random Search methods. This study reveals that while model selection is crucial, an appropriate optimization method and imputation technique significantly impact model performance. These findings provide valuable insights for developing robust predictive models for healthcare applications, particularly for heart failure risk assessment. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Data Analysis and Healthcare Virtual Assistants)
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15 pages, 1480 KiB  
Article
Assessing the Capability of Advanced AI Models in Cardiovascular Symptom Recognition: A Comparative Study
by Jordi Cusidó, Lluc Solé-Vilaró, Pere Marti-Puig and Jordi Solé-Casals
Appl. Sci. 2024, 14(18), 8440; https://doi.org/10.3390/app14188440 - 19 Sep 2024
Cited by 3 | Viewed by 1326
Abstract
The field of medical informatics has been significantly transformed in recent years with the emergence of Natural Language Understanding (NLU) and Large Language Models (LLM), providing new opportunities for innovative patient care solutions. This study aims to evaluate the effectiveness of publicly available [...] Read more.
The field of medical informatics has been significantly transformed in recent years with the emergence of Natural Language Understanding (NLU) and Large Language Models (LLM), providing new opportunities for innovative patient care solutions. This study aims to evaluate the effectiveness of publicly available LLMs as symptom checkers for cardiological diseases by comparing their diagnostic capabilities in real disease cases. We employed a set of 9 models, including ChatGPT-4, OpenSource models, Google PaLM 2, and Meta’s LLaMA, to assess their diagnostic accuracy, reliability, and safety across various clinical scenarios. Our methodology involved presenting these LLMs with symptom descriptions and test results in Spanish, requiring them to provide specialist diagnoses and recommendations in English. This approach allowed us to compare the performance of each model, highlighting their respective strengths and limitations in a healthcare context. The results revealed varying levels of accuracy, precision, and sensitivity among the models, demonstrating the potential of LLMs to enhance medical education and patient care. By analysing the capabilities of each model, our study contributes to a deeper understanding of artificial intelligence’s role in medical diagnosis. We argue for the strategic implementation of LLMs in healthcare, emphasizing the importance of balancing sensitivity and realism to optimize patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Medical Data Analysis and Healthcare Virtual Assistants)
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