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Electronics
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Artificial Intelligence and Deep Learning Techniques for Healthcare
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Artificial Intelligence and Deep Learning Techniques for Healthcare

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4835

Special Issue Editors

Prof. Dr. Massimo Esposito

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), 80131 Naples, Italy
Interests: artificial intelligence; natural language processing; knowledge engineering; conversational systems
Special Issues, Collections and Topics in MDPI journals
Dr. Umberto Maniscalco

E-Mail Website
Guest Editor
Institute of Computing and High-Performance Networks, Department of Engineering, ICT and Technology for Energy and Transport, National Research Council (CNR), Naples, Italy
Interests: artificial intelligence; robotics; social robotics; healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Antonietta Panza

E-Mail Website
Guest Editor
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via P. Castellino 111, 80131 Naples, Italy
Interests: AI; data analysis
Dr. Francesco Gargiulo

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking, ICAR-CNR, Via Pietro Castellino 111, 80131 Naples, Italy
Interests: artificial intelligence; deep learning; natural language processing; big data analytics; quantum computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing availability of biomedical data, ranging from electronic health records and medical imaging to -omics data, sensor outputs, and clinical text, presents significant opportunities to enhance healthcare through advanced computational methods. However, these data sources are often complex, heterogeneous, poorly annotated, and unstructured, creating significant challenges in their utilization. Extracting actionable insights from such high-dimensional and intricate datasets is crucial for advancing medical research and practice. Recent advancements in deep learning have led to the development of end-to-end algorithms capable of automatically extracting meaningful features from large and unstructured data. Numerous initiatives have already explored the application of deep learning in healthcare, demonstrating substantial progress in various areas. Nevertheless, these methodologies have yet to be comprehensively evaluated with regard to the diverse range of medical fields that stand to benefit from their capabilities.

We invite you to contribute to this Special Issue dedicated to advancing research on deep learning in healthcare.

This Special Issue will delve into the current landscape of deep learning applications and investigate cutting-edge innovations at the forefront of this field. We welcome submissions that provide technical, experimental, methodological, and conceptual insights into the evolving research landscape of deep learning within the healthcare domain.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Generative AI methods;
  • Multi-modal models;
  • Hybrid architectures;
  • Data generation and synthetic data;
  • Large language model (LLM) applications;
  • Computer vision algorithms;
  • Vision transformers;
  • Pattern recognition algorithms;
  • Deep reinforcement learning;
  • Medical image analysis (MRI, CT scans, X-rays, pathology);
  • Electronic Health Record (EHR) data mining;
  • Federated learning techniques;
  • Voice and speech recognition in healthcare;
  • Natural Language Processing (NLP) in human–robot interactions and NLP;
  • Human motion analysis;
  • Pose estimation.

We look forward to receiving your contributions.

Prof. Dr. Massimo Esposito
Dr. Umberto Maniscalco
Dr. Maria Antonietta Panza
Dr. Francesco Gargiulo
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. Electronics 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

  • computer vision
  • pattern recognition
  • generative AI
  • large language model
  • natural language processing
  • synthetic data
  • multi-modal models
  • hybrid networks
  • biomedical informatics

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Published Papers (3 papers)

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18 pages, 259 KiB  
Article
Deep Learning for Predicting Rehabilitation Success: Advancing Clinical and Patient-Reported Outcome Modeling
by Yasser Mahmoud, Kaleb Horvath and Yi Zhou
Electronics 2025, 14(6), 1082; https://doi.org/10.3390/electronics14061082 - 9 Mar 2025
Viewed by 987
Abstract
Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid [...] Read more.
Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to predict rehabilitation success based on clinical and patient-reported outcome measures (CROMs and PROMs). Using a dataset of 1047 rehabilitation patients encompassing diverse musculoskeletal conditions and treatment protocols, we compare the performance of deep learning models with previously established machine learning approaches such as Random Forest and Extra Trees classifiers. Our findings reveal that deep learning significantly enhances predictive performance. The weighted F1-score for direct classification improved from 65% to 74% using a CNN-RNN architecture, and the mean absolute error (MAE) for regression-based success metrics decreased by 12%, translating to more precise estimations of functional recovery. These improvements hold clinical significance as they enhance the ability to tailor rehabilitation interventions to individual patient needs, potentially optimizing recovery timelines and resource allocation. Moreover, attention mechanisms integrated into the deep learning models provided improved interpretability, highlighting key predictors such as age, range of motion, and PROM indices. This study underscores the potential of deep learning to advance outcome prediction in rehabilitation, offering more precise and interpretable tools for clinical decision-making. Future work will explore real-time applications and the integration of multimodal data to further refine these models. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning Techniques for Healthcare)
17 pages, 13771 KiB  
Article
Recommendation Method Based on Glycemic Index for Intake Order of Foods Detected by Deep Learning
by Jae-young Lee and Soon-kak Kwon
Electronics 2025, 14(3), 457; https://doi.org/10.3390/electronics14030457 - 23 Jan 2025
Viewed by 798
Abstract
In this paper, we propose a recommendation method for food intake order based on the glycemic index (GI) using deep learning to reduce rapid blood sugar spikes during meals. The foods in a captured image are classified through a food detection network. The [...] Read more.
In this paper, we propose a recommendation method for food intake order based on the glycemic index (GI) using deep learning to reduce rapid blood sugar spikes during meals. The foods in a captured image are classified through a food detection network. The GIs for the detected foods are found by matching their names or categories with the information stored in the database. If the detected food name or category is not found in the database, the food information is found from a public API. The food is classified into one of the food categories based on nutrients, and the median GI of the corresponding category is assigned to the food. The food intake order is recommended from the lowest to the highest GI. We implemented a web service that visualizes the food analysis results and the recommended food intake order. In experimental results, the average inference time and accuracy were 57.1 ms and 98.99% for Mask R-CNN, respectively, and 24.4 ms and 91.72% for YOLOv11, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning Techniques for Healthcare)
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42 pages, 11126 KiB  
Systematic Review
A Systematic Review of Serious Games in the Era of Artificial Intelligence, Immersive Technologies, the Metaverse, and Neurotechnologies: Transformation Through Meta-Skills Training
by Eleni Mitsea, Athanasios Drigas and Charalabos Skianis
Electronics 2025, 14(4), 649; https://doi.org/10.3390/electronics14040649 - 7 Feb 2025
Cited by 1 | Viewed by 3081
Abstract
Background: Serious games (SGs) are primarily aimed at promoting learning, skills training, and rehabilitation. Artificial intelligence, immersive technologies, the metaverse, and neurotechnologies promise the next revolution in gaming. Meta-skills are considered the “must-have” skills for thriving in the era of rapid change, complexity, [...] Read more.
Background: Serious games (SGs) are primarily aimed at promoting learning, skills training, and rehabilitation. Artificial intelligence, immersive technologies, the metaverse, and neurotechnologies promise the next revolution in gaming. Meta-skills are considered the “must-have” skills for thriving in the era of rapid change, complexity, and innovation. Μeta-skills can be defined as a set of higher-order skills that incorporate metacognitive, meta-emotional, and meta-motivational attributes, enabling one to be mindful, self-motivated, self-regulated, and flexible in different circumstances. Skillfulness, and more specifically meta-skills development, is recognized as a predictor of optimal performance along with mental and emotional wellness. Nevertheless, there is still limited knowledge about the effectiveness of integrating cutting-edge technologies in serious games, especially in the field of meta-skills training. Objectives: The current systematic review aims to collect and synthesize evidence concerning the effectiveness of advanced technologies in serious gaming for promoting meta-skills development. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to identify experimental studies conducted in the last 10 years. Four different databases were employed: Web of Science, PubMed, Scopus, and Google Scholar. Results: Forty-nine studies were selected. Promising outcomes were identified in AI-based SGs (i.e., gamified chatbots) as they provided realistic, adaptive, personalized, and interactive environments using natural language processing, player modeling, reinforcement learning, GPT-based models, data analytics, and assessment. Immersive technologies, including the metaverse, virtual reality, augmented reality, and mixed reality, provided realistic simulations, interactive environments, and sensory engagement, making training experiences more impactful. Non-invasive neurotechnologies were found to encourage players’ training by monitoring brain activity and adapting gameplay to players’ mental states. Healthy participants (n = 29 studies) as well as participants diagnosed with anxiety, neurodevelopmental disorders, and cognitive impairments exhibited improvements in a wide range of meta-skills, including self-regulation, cognitive control, attention regulation, meta-memory skills, flexibility, self-reflection, and self-evaluation. Players were more self-motivated with an increased feeling of self-confidence and self-efficacy. They had a more accurate self-perception. At the emotional level, improvements were observed in emotional regulation, empathy, and stress management skills. At the social level, social awareness was enhanced since they could more easily solve conflicts, communicate, and work in teams. Systematic training led to improvements in higher-order thinking skills, including critical thinking, problem-solving skills, reasoning, decision-making ability, and abstract thinking. Discussion: Special focus is given to the potential benefits, possible risks, and ethical concerns; future directions and implications are also discussed. The results of the current review may have implications for the design and implementation of innovative serious games for promoting skillfulness among populations with different training needs. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning Techniques for Healthcare)
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