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Artificial Intelligence Innovations for Smart and Sustainable Healthcare

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

Deadline for manuscript submissions: 30 October 2026 | Viewed by 9116

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Special Issue Editors

Dr. Martina Iammarino

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Guest Editor

Department of Computer Science and Technologies, Pegaso University, 80143 Naples, Italy
Interests: e-health; artificial intelligence; explainability
Special Issues, Collections and Topics in MDPI journals

Dr. Chiara Verdone

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Guest Editor

Department of Agricultural Science, Food, Natural Resources and Engineering, University of Foggia, 71122 Foggia, Italy
Interests: medical decision support; healthcare AI; explainable AI

Dr. Stefano Galantucci

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Guest Editor

Department of Agricultural Science, Food, Natural Resources and Engineering, University of Foggia, 71122 Foggia, Italy
Interests: e-health security; artificial intelligence security; biometrics

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into healthcare is driving a paradigm shift toward smarter, more efficient, and sustainable medical systems. This Special Issue aims to explore the latest innovations in AI techniques and their transformative impact on healthcare delivery, diagnostics, patient monitoring, and decision support. We invite original research, review articles, and case studies that highlight novel algorithms, real-world clinical applications, and interdisciplinary approaches involving data science, medical practice, and digital technologies. Particular attention will be given to works that promote transparency, scalability, and sustainability in healthcare solutions powered by AI. At the same time, given the critical nature of medical data and infrastructures, we encourage contributions addressing the robustness and resilience of AI systems in e-health settings. This includes secure and privacy-aware models, trustworthy data processing frameworks, and intelligent communication protocols specifically designed for healthcare environments.The goal is to foster a scientific dialogue that bridges the gap between technological advances and practical implementation in diverse healthcare environments while ensuring the reliability, security, and long-term sustainability of AI-powered healthcare systems.

Dr. Martina Iammarino
Dr. Chiara Verdone
Dr. Stefano Galantucci
Guest Editors

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Keywords

machine learning
smart healthcare
sustainable healthcare
clinical decision support
health informatics
explainable AI
artificial intelligence in medicine
digital health
privacy-preserving AI
robust AI systems
secure e-health protocols
medical data security
trustworthy AI

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Published Papers (9 papers)

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Research

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22 pages, 793 KB

Open AccessArticle

Comparative Analysis of Machine Learning and Deep Learning Models for Atrial Fibrillation Detection from Long-Term ECG

by Lerina Aversano, Ilaria Mancino, Agostino Marengo and Chiara Verdone

Appl. Sci. 2026, 16(5), 2390; https://doi.org/10.3390/app16052390 - 28 Feb 2026

Viewed by 353

Abstract

Atrial fibrillation is the most prevalent sustained cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature mortality. Automatic detection remains challenging due to the variability of electrocardiogram (ECG) morphology, noise, and the paroxysmal nature of atrial fibrillation events. This study proposes a comprehensive framework that integrates optimised segmentation, feature extraction, and advanced deep learning architectures to improve detection accuracy. A coalescence window is introduced to dynamically cluster arrhythmic episodes, aligning computational analysis with clinical event distributions. Multiple classifiers are investigated, ranging from traditional machine learning models to state-of-the-art deep neural networks, including Temporal Convolutional Networks (TCNs), Convolutional Neural Networks (CNNs), and Bidirectional Long Short-Term Memory (BiLSTM). Experimental evaluation on a balanced dataset of ECG signals demonstrates the superior performance of deep learning models, with the best architecture achieving high accuracy and F1-score, significantly outperforming traditional approaches. Furthermore, the proposed pipeline is designed to be modular and resource-aware, supporting potential deployment in real-time and edge computing environments. These results highlight the feasibility of scalable atrial fibrillation monitoring systems that bridge algorithmic innovation with clinical applicability, ultimately contributing to earlier diagnosis and improved patient management. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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28 pages, 2434 KB

Open AccessArticle

Artificial Intelligence–Enabled Dementia Risk Prediction for Smart and Sustainable Healthcare: An Interpretable Machine Learning Study Using NHATS

by Ashrafe Alam, Md Golam Rabbani and Victor R. Prybutok

Appl. Sci. 2026, 16(5), 2180; https://doi.org/10.3390/app16052180 - 24 Feb 2026

Viewed by 547

Abstract

Dementia is an increasing public health challenge, yet scalable methods for early risk detection using non-clinical data remain limited. This study develops and evaluates interpretable machine learning models to predict dementia risk among older adults using nationally representative longitudinal data. Data were sourced from the National Health and Aging Trends Study (NHATS, 2011–2022) and included 5984 community-dwelling U.S. adults aged 65 and older who were dementia-free at baseline. Dementia onset was identified using the validated NHATS classification algorithm based on cognitive assessments, proxy reports, and physician diagnoses. After data preprocessing and feature engineering, missing values in continuous variables were imputed with k-nearest neighbors, while categorical variables were handled via one-hot encoding and mode-based imputation. Five supervised machine learning algorithms were trained and evaluated through stratified cross-validation, using performance metrics that account for class imbalance. Among these models, XGBoost showed the strongest overall performance, achieving the highest classification accuracy (0.881 ± 0.004), the lowest Brier score (0.094 ± 0.002), and the highest ROC–AUC (0.823 ± 0.005), with RF showing comparable results. Explainable AI analyses with SHapley Additive exPlanations (SHAP) consistently identified digital technology use, outdoor activity frequency, and social network size as the most influential predictors across models. These findings indicate that interpretable machine learning based on non-clinical, modifiable behavioral and social factors can support scalable, prevention-focused dementia risk assessment and inform prevention-oriented strategies that promote digital inclusion and social engagement among older adults. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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16 pages, 803 KB

Open AccessArticle

AI-Powered Physiotherapy: Evaluating LLMs Against Students in Clinical Rehabilitation Scenarios

by Ioanna Michou, Athanasios Fouras, Dionysia Chrysanthakopoulou, Marina Theodoritsi, Savina Mariettou, Sotiria Stellatou and Constantinos Koutsojannis

Appl. Sci. 2026, 16(3), 1165; https://doi.org/10.3390/app16031165 - 23 Jan 2026

Viewed by 1331

Abstract

Generative artificial intelligence (GenAI), particularly large language models (LLMs) such as ChatGPT and DeepSeek, is transforming healthcare by enhancing clinical decision-making, education, and patient interaction. This exploratory study compares the responses of ChatGPT (GPT-4.1) and DeepSeek-V2 against 90 final-year physiotherapy students in Greece on the quality of the responses to 60 clinical questions across four rehabilitation domains: low back pain, multiple sclerosis, frozen shoulder, and knee osteoarthritis (15 questions per domain). The questions spanned basic knowledge, diagnosis, alternative treatments, and rehabilitation practices. The responses were evaluated for their relevance, accuracy, clarity, completeness, and consistency with clinical practice guidelines (CPGs), emphasizing conceptual understanding. This study provides novel contributions by (i) benchmarking LLMs in physiotherapy-specific domains (low back pain, multiple sclerosis, frozen shoulder, and knee osteoarthritis) underrepresented in prior AI-health evaluations; (ii) directly comparing the LLM written response quality to student performance under exam constraints; and (iii) highlighting the improvement potential for education, complementing ChatGPT’s established role in physician decision support. The results indicate that the LLMs produced higher-quality written responses than students in most domains, particularly in the global response quality and the conceptual depth of written responses, highlighting their potential as educational aids for knowledge-based tasks, although not equivalent to clinical expertise. This suggests AI’s role in physiotherapy as a supportive tool rather than a replacement for hands-on clinical skills and asks whether GenAI could transform physiotherapy practice by augmenting, rather than threatening, human-centered care, for its potential as a knowledge support tool in education, pending validation in clinical contexts. This study explores these findings, compares them with the related work, and discusses whether GenAI will transform or threaten physiotherapy practice. Ethical considerations, limitations, and future directions, including AI voice assistants and AI characters, are addressed. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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21 pages, 5181 KB

Open AccessArticle

AI-Based Image Time-Series Analysis of the Niacin Skin Flush Test in Schizophrenia and Bipolar Disorder

by Ryszard Sitarz, Arkadiusz Syta, Robert Karpiński, Anna Machrowska, Joanna Róg, Kaja Karakuła, Dariusz Juchnowicz and Hanna Karakuła-Juchnowicz

Appl. Sci. 2025, 15(23), 12368; https://doi.org/10.3390/app152312368 - 21 Nov 2025

Viewed by 739

Abstract

Psychotic disorders such as schizophrenia (SCH) and bipolar affective disorder (BD) are associated with lipid metabolism abnormalities and inflammatory dysregulation. The niacin skin flush test (NSFT) has long been investigated as a non-invasive indicator of these disturbances. This study used deep learning models to assess the diagnostic utility of SKINREMS, a computerized system for automated temporal analysis of skin flush responses. The study included a total of 188 participants, comprising individuals with psychotic disorders and healthy controls. Sequential skin images were recorded after topical application of methyl nicotinate. Five convolutional neural network architectures—ResNet50, ResNet101, DenseNet121, InceptionV3, and EfficientNetB0—were evaluated for their performance in analyzing these time-dependent dermatological responses in a binary classification task. Accuracy, precision, recall, F1-score, and AUC were calculated at four time points (frames 1, 10, 20, 30). The models demonstrated distinct temporal performance profiles. ResNet50 showed consistent high performance across all time points, making it suitable for clinical environments requiring stable predictions. DenseNet121 achieved the highest AUC (up to 0.99) after 15 min, indicating its potential in extended monitoring. EfficientNetB0 offered gradual performance improvement with lower computational demands, while InceptionV3 was most effective at intermediate time points. ResNet101 showed initial high performance but declined mid-phase. AUC remained stable across all models, suggesting robust discriminative capability over time. This study highlights the importance of selecting appropriate deep learning architectures based on the temporal dynamics of biological responses. The findings demonstrate potential for future clinical application of AI in non-invasive diagnostics of psychotic spectrum disorders. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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19 pages, 2457 KB

Open AccessArticle

A Logic Tensor Network-Based Neurosymbolic Framework for Explainable Diabetes Prediction

by Semanto Mondal, Antonino Ferraro, Fabiano Pecorelli and Giuseppe De Pietro

Appl. Sci. 2025, 15(21), 11806; https://doi.org/10.3390/app152111806 - 5 Nov 2025

Cited by 2 | Viewed by 2003

Abstract

Neurosymbolic AI is an emerging paradigm that combines neural network learning capabilities with the structured reasoning capacity of symbolic systems. Although machine learning has achieved cutting-edge outcomes in diverse fields, including healthcare, agriculture, and environmental science, it has potential limitations. Machine learning and neural models excel at identifying intricate data patterns, yet they often lack transparency, depend on large labelled datasets, and face challenges with logical reasoning and tasks that require explainability. These challenges reduce their reliability in high-stakes applications such as healthcare. To address these limitations, we propose a hybrid framework that integrates symbolic knowledge expressed in First-Order Logic into neural learning via a Logic Tensor Network (LTN). In this framework, expert-defined medical rules are embedded as logical axioms with learnable thresholds. As a result, the model gains predictive power, interpretability, and explainability through reasoning over the logical rules. We have utilized this neurosymbolic method for predicting diabetes by employing the Pima Indians Diabetes Dataset. Our experimental setup evaluates the LTN-based model against several conventional methods, including Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (K-NN), Random Forest Classifiers (RF), Naive Bayes (NB), and a Standalone Neural Network (NN). The findings demonstrate that the neurosymbolic framework not only surpasses traditional models in predictive accuracy but also offers improved explainability and robustness. Notably, the LTN-based neurosymbolic framework achieves an excellent balance between recall and precision, along with a higher AUC-ROC score. These results underscore its potential for trustworthy medical diagnostics. This work highlights how integrating symbolic reasoning with data-driven models can bridge the gap between explainability, interpretability, and performance, offering a promising direction for AI systems in domains where both accuracy and explainability are critical. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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17 pages, 465 KB

Open AccessArticle

From Knowledge Extraction to Assertive Response: An LLM Chatbot for Information Retrieval in Telemedicine Systems

by Bruna D. Pupo, Daniel G. Costa, Roger Immich, Aldo von Wangenheim, Alex Sandro Roschildt Pinto and Douglas D. J. de Macedo

Appl. Sci. 2025, 15(21), 11732; https://doi.org/10.3390/app152111732 - 3 Nov 2025

Cited by 1 | Viewed by 1065

Abstract

The development of new technologies, improved by advances in artificial intelligence, has enabled the creation of a new generation of applications in different scenarios. In medical systems, adopting AI-driven solutions has brought new possibilities, but their effective impacts still need further investigation. In this context, a chatbot prototype trained with large language models (LLMs) was developed using data from the Santa Catarina Telemedicine and Telehealth System (STT) Dermatology module. The system adapts Llama 3 8B via supervised Fine-tuning with QLoRA on a proprietary, domain-specific dataset (33 input-output pairs). Although it achieved 100% Fluency and 89.74% Coherence, Factual Correctness remained low (43.59%), highlighting the limitations of training LLMs on small datasets. In addition to G-Eval metrics, we conducted expert human validation, encompassing both quantitative and qualitative aspects. This low factual score indicates that a retrieval-augmented generation (RAG) mechanism is essential for robust information retrieval, which we outline as a primary direction for future work. This approach enabled a more in-depth analysis of a real-world telemedicine environment, highlighting both the practical challenges and the benefits of implementing LLMs in complex systems, such as those used in telemedicine. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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Review

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21 pages, 484 KB

Open AccessReview

Artificial Intelligence in Neonatal Respiratory Care: Current Applications and Future Directions

by Aikaterini Nikolaou, Maria Baltogianni, Niki Dermitzaki, Nikitas Chatzigiannis, Dimitra Savidou, Sevastianos Geitonas, Lida-Eleni Giaprou and Vasileios Giapros

Appl. Sci. 2026, 16(3), 1339; https://doi.org/10.3390/app16031339 - 28 Jan 2026

Viewed by 740

Abstract

Respiratory disorders remain a major cause of morbidity and mortality in neonatal intensive care units, particularly among preterm infants. Advances in physiological monitoring, medical imaging, and electronic health records have enabled the growing application of artificial intelligence in neonatal respiratory care. This narrative review summarizes current applications and emerging directions of artificial intelligence in the diagnosis, monitoring, and management of neonatal respiratory disorders. Machine learning and deep learning approaches have demonstrated promising performance in respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity, ventilatory management, and severe respiratory complications. By integrating multimodal clinical, physiological, and imaging data, these methods support earlier detection of respiratory deterioration and improved clinical decision-making. However, challenges related to data quality, generalizability, interpretability, and limited prospective validation continue to constrain widespread clinical implementation, highlighting the need for careful integration into neonatal care workflows. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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25 pages, 3111 KB

Open AccessReview

From Local to Global Perspective in AI-Based Digital Twins in Healthcare

by Maciej Piechowiak, Aleksander Goch, Ewelina Panas, Jolanta Masiak, Dariusz Mikołajewski, Izabela Rojek and Emilia Mikołajewska

Appl. Sci. 2026, 16(1), 83; https://doi.org/10.3390/app16010083 - 21 Dec 2025

Cited by 1 | Viewed by 1063

Abstract

Digital twins (DTs) powered by artificial intelligence (AI) are becoming important transformational tools in healthcare, enabling real-time simulation and personalized decision support at the patient level. The aim of this review is to critically examine the evolution, current applications, and future potential of AI-based DTs in healthcare, with a particular focus on their role in enabling real-time simulation and personalized patient-level decision support. Specifically, the review aims to provide a comprehensive overview of how AI-based DTs are being developed and implemented in various clinical domains, identifying existing scientific and technical gaps and highlighting methodological, regulatory, and ethical issues. Taking a “local to global” perspective, the review aims to explore how individual patient-level models can be scaled and integrated to inform population health strategies, global data networks, and collaborative research ecosystems. This will provide a structured foundation for future research, clinical applications, and policy development in this rapidly evolving field. Locally, DTs allow medical professionals to model individual patient physiology, predict disease progression, and optimize treatment strategies. Hospitals are implementing AI-based DT platforms to simulate workflows, efficiently allocate resources, and improve patient safety. Generative AI further enhances these applications by creating synthetic patient data for training, filling gaps in incomplete records, and enabling privacy-respecting research. On a broader scale, regional health systems can use connected DTs to model population health trends and predict responses to public health interventions. On a national scale, governments and policymakers can use these insights for strategic planning, resource allocation, and increasing resilience to health crises. Internationally and globally, AI-based DTs can integrate diverse datasets across borders to support research collaboration and improve early pandemic detection. Generative AI contributes to global efforts by harmonizing heterogeneous data, creating standardized virtual patient cohorts, and supporting cross-cultural medical education. Combining local precision with global insights highlights DTs’ role as a bridge between personalized and global health. Despite the efforts of medical and technical specialists, ethical, regulatory, and data governance challenges remain crucial to ensuring responsible and equitable implementation worldwide. In conclusion, AI-based DTs represent a transformative paradigm, combining individual patient care with systemic and global health management. These perspectives highlight the potential of AI-based DTs to bridge precision medicine and public health, provided ethical, regulatory, and governance challenges are addressed responsibly. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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Other

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25 pages, 1693 KB

Open AccessSystematic Review

Weakly Supervised Deep Learning for Ocular Image Segmentation: A Systematic Review of Fundus and OCT Methods

by Pedro Penedo, Jorge Machado, Rita Anjos, Ana Marta, Aristófanes Corrêa Silva and António Cunha

Appl. Sci. 2026, 16(5), 2241; https://doi.org/10.3390/app16052241 - 26 Feb 2026

Viewed by 579

Abstract

Eye diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration, drive the growing need for reliable and scalable analyses of fundus and optical coherence tomography (OCT) images. Deep learning performs strongly in ocular structure segmentation. However, it typically relies on dense pixel-wise annotations, which are costly and difficult to obtain at scale. Weakly supervised learning (WSL) can reduce this burden by leveraging coarse labels, limited strong annotations, and unlabeled data. This systematic umbrella review synthesizes survey and review articles on weakly supervised deep learning for image segmentation, with a focus on ocular imaging (fundus and OCT/OCTA). After analyzing twenty-one secondary studies, the main finding reveals an “empty intersection”: WSL-focused segmentation surveys are often modality-agnostic. Conversely, ocular reviews are predominantly fully supervised and seldom offer quantitative evidence on annotation-effort savings or direct comparisons between weak and fully supervised methods on identical datasets. Across the included reviews, label-efficient strategies cluster around CAM/MIL formulations, sparse supervision (points/scribbles/boxes), pseudo-labelling/self-training, and semi-/self-supervised learning, implemented mainly with U-Net/DeepLab families and increasingly Transformer or hybrid backbones. These results provide a structured map of available WSL mechanisms and, critically, identify reproducible reporting gaps that currently prevent fair benchmarking in ocular segmentation. Therefore, this review supports the development of ocular-specific benchmarks and minimum reporting practices that link segmentation performance to annotation effort. Full article

(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)

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