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Applied Sciences
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IoT, Robots, and Generative AI in Clinical Engineering: Developments and...
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IoT, Robots, and Generative AI in Clinical Engineering: Developments and Applications

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 September 2025 | Viewed by 877

Special Issue Editor

Dr. Alessio Luschi

E-Mail Website
Guest Editor
Department of Medical Biotechnologies, University of Siena, Italy
Interests: biomedical engineering; clinical engineering; natural language processing

Special Issue Information

Dear Colleagues,

In recent years, the healthcare industry has experienced a profound transformation driven by the integration of advanced technologies, significantly impacting clinical engineering practices. Among the most influential technologies revolutionizing how healthcare is delivered, managed, and optimized are the Internet of Things (IoT), robotics, and generative AI. The IoT enables seamless real-time monitoring, data collection, and communication across medical devices and systems, enhancing patient care and operational efficiency. Robotics is advancing precision in surgical procedures, improving rehabilitation, and automating various hospital functions. Furthermore, generative AI is contributing to treatment plans’ personalization, enhancing diagnostic accuracy, and supporting clinical decision-making processes.

By covering a wide range of topics, including theoretical advancements, practical applications, case studies, and ethical considerations, this Special Issue aims to provide an in-depth exploration of how these cutting-edge technologies are being implemented in clinical settings and their broader implications for the field, offering a comprehensive overview of the current state and future potential in clinical engineering, ultimately aiming to advance healthcare innovation.

Dr. Alessio Luschi
Guest Editor

Manuscript Submission Information

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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

  • IoT
  • robotics
  • generative AI
  • natural language processing
  • artificial intelligence
  • clinical engineering
  • health technology management
  • health technology assessment
  • decision support
  • biomedical engineering

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Published Papers (1 paper)

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Research

21 pages, 3092 KiB  
Article
Deep Learning for Risky Cardiovascular and Cerebrovascular Event Prediction in Hypertensive Patients
by Francesco Goretti, Ali Salman, Alessandra Cartocci, Alessio Luschi, Leandro Pecchia, Massimo Milli and Ernesto Iadanza
Appl. Sci. 2025, 15(3), 1178; https://doi.org/10.3390/app15031178 - 24 Jan 2025
Viewed by 607
Abstract
In this comprehensive study, we employed a versatile approach to tackle the prediction challenges associated with atrial fibrillation (AF) and cardiovascular events (CE). Exploiting the Gaussian copula synthesizer technique for data generation, we created high-quality synthetic data to overcome the limitations posed by [...] Read more.
In this comprehensive study, we employed a versatile approach to tackle the prediction challenges associated with atrial fibrillation (AF) and cardiovascular events (CE). Exploiting the Gaussian copula synthesizer technique for data generation, we created high-quality synthetic data to overcome the limitations posed by scarce patient records. Heart rate variability (HRV), known to be an efficient indicator of cardiac health often used with artificial intelligence (AI), was used to train and optimize custom-built deep learning (DL) models. Additionally, we explored transfer learning (TL) to enhance the model capabilities by adapting our AF classification model to address CE classification challenges, effectively transferring learned features and patterns, without extensive retraining. As a result, our models achieved accuracy rates of 77% for AF and 82% for CEs, with high sensitivity, highlighting the efficacy of synthetic data generation and transfer learning in improving classification performance across diverse medical datasets. These findings hold significant promise for enhancing diagnostic and predictive capabilities in clinical settings, ultimately contributing to improved patient care and outcomes. Full article
(This article belongs to the Special Issue IoT, Robots, and Generative AI in Clinical Engineering: Developments and Applications)
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Figure 1

Figure 1
<p>Schematic overview of HRV classification pipeline: (1) data preprocessing (undersampling, feature selection, shuffling, and train/test split), (2) synthetic data generation, (3) data normalization (0–1), (4) deep learning model trained on AF data, and (5) transfer learning of AF model and fine-tuning on CE data. All the preprocessing steps were the same but applied separately to both datasets.</p> Full article ">Figure 2
<p>Box plots comparing two examples of HRV features, SDNN (ms) and pNNxx (%), between synthetic (blue) and real (green) data for cardiovascular events (CE). Each box shows the median (horizontal line), interquartile range (IQR, box boundaries), and mean (represented by the triangle inside the box), with whiskers indicating the data range. <span class="html-italic">p</span>-values (SDNN: 0.840, pNNxx: 0.827) are displayed inside the chart, highlighting the similarity between the synthetic and real data distributions.</p> Full article ">Figure A1
<p>Feature importance in atrial fibrillation (AF) dataset. Top 20 important HRV features for AF classification.</p> Full article ">Figure A2
<p>Feature importance in cardiovascular events (CE) dataset. Top 20 important HRV features for CE classification.</p> Full article ">
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