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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 3115

Special Issue Editor

Dr. Alessio Luschi

E-Mail Website
Guest Editor
Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
Interests: clinical engineering; biomedical engineering; natural language processing; generative AI
Special Issues, Collections and Topics in MDPI journals

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

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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 (2 papers)

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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 870
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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21 pages, 413 KiB  
Systematic Review
A Systematic Literature Review of Eye-Tracking and Machine Learning Methods for Improving Productivity and Reading Abilities
by Lewis Arnold, Soniya Aryal, Brandon Hong, Mahiethan Nitharsan, Anaya Shah, Waasiq Ahmed, Zakariya Lilani, Wanzi Su and Davide Piaggio
Appl. Sci. 2025, 15(6), 3308; https://doi.org/10.3390/app15063308 - 18 Mar 2025
Viewed by 1898
Abstract
Deteriorating eyesight is increasingly prevalent in the digital age due to prolonged screen exposure and insufficient eye care, leading to reduced productivity and difficulties in maintaining focus during extended reading sessions. This systematic literature review, following PRISMA guidelines, evaluates 1782 articles, with 42 [...] Read more.
Deteriorating eyesight is increasingly prevalent in the digital age due to prolonged screen exposure and insufficient eye care, leading to reduced productivity and difficulties in maintaining focus during extended reading sessions. This systematic literature review, following PRISMA guidelines, evaluates 1782 articles, with 42 studies ultimately included, assessing their quality using the Mixed Methods Appraisal Tool (MMAT). The selected studies are categorised into eye metric classification, measuring comprehension, measuring attention, and typography and typesetting. Recent advances have demonstrated the potential of machine learning to enhance eye movement predictions, such as the classification of fixations and saccades, while other research utilises eye metrics to assess mental fatigue and attention levels. Additionally, modifications to typography have been explored as a means of improving focus and memory retention. The findings highlight the transformative role of eye-tracking technologies and machine learning in understanding reading behaviour, attention, and cognitive workload. However, challenges such as data scarcity, limited generalisability, and biases in existing methodologies persist. Addressing these gaps through standardised frameworks, diverse datasets, and advancements in synthetic data generation could enhance the accessibility, accuracy, and real-world applicability of eye-tracking solutions for improving reading comprehension and focus. Full article
(This article belongs to the Special Issue IoT, Robots, and Generative AI in Clinical Engineering: Developments and Applications)
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