Digital Health: AI-Driven Personalized Healthcare and Applications

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "Medical & Healthcare AI".

Deadline for manuscript submissions: 16 November 2026 | Viewed by 2160

Special Issue Editors


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Guest Editor
Institute for High Performance Computing and Networks (ICAR) of the National Research Council of Italy (CNR), Naples, Italy
Interests: artificial intelligence; signal analysis; pattern recognition; interpretability and explainability; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Mathematics and Physics, Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy
Interests: signal processing; m-health system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networks (ICAR) of the National Research Council of Italy (CNR), Naples, Italy
Interests: machine learning; explainable artificial intelligence

Special Issue Information

Dear Colleagues,

Recent advancements in Artificial Intelligence (AI) are revolutionizing the healthcare sector by enabling more personalized, precise, predictive, and efficient care. The integration of AI with digital health technologies, including wearable sensors, electronic health records, and mobile platforms, provides a unique opportunity to customize treatments for individual patients, enhance clinical decision-making, and promote preventive strategies.

However, despite Artificial Intelligence's transformative potential in healthcare, its adoption faces considerable challenges. Protecting sensitive data, preventing cyberattacks, and ensuring fair and explainable algorithms remain crucial concerns.

Based on the above considerations, we are pleased to invite you to contribute to this Special Issue, 'Digital Health: AI-Driven Personalized Healthcare and Applications', which aims to highlight novel methodologies, real-world applications, and critical insights into how AI can improve healthcare delivery through personalization. This topic lies at the core of the journal’s mission, addressing computational innovations and intelligent systems that enhance medical diagnosis, prognosis, and patient monitoring.

This Special Issue seeks to bridge the gap between data science, clinical practice, and biomedical research by showcasing interdisciplinary efforts focusing on the development, validation, and deployment of AI models in healthcare. Unlike general-purpose AI in medicine, the focus here is on systems that adapt to individual variability, capturing the complexity of human health through personalized analytics. 

We welcome contributions that explore theoretical frameworks and practical implementations, as well as reviews that summarize progress and outline future directions. Submissions may address, but are not limited to, the following topics:

  • AI for patient stratification and outcome prediction;
  • Personalized treatment planning and precision medicine;
  • Image analysis and signal processing for personalized and precision medicine;
  • Federated learning and data privacy in clinical AI;
  • Explainable AI (XAI) and model transparency in healthcare;
  • Integration of AI with mobile health and wearable technologies;
  • Real-world applications and deployment studies;
  • Multimodal data fusion (e.g. imaging and sensor data) for personalized solutions;
  • Simulation and Digital Twin technologies for personalized treatment testing;
  • Technologies for protecting sensitive data and preventing cyberattacks;
  • Ethical and societal considerations in AI-driven

We look forward to hearing from you.

Dr. Giovanna Sannino
Dr. Laura Verde
Dr. Salvatore Giugliano
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 250 words) can be sent to the Editorial Office for assessment.

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. AI is an international peer-reviewed open access monthly 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 1800 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
  • personalized healthcare
  • digital health
  • predictive modelling
  • precision medicine
  • wearable devices
  • explainable AI
  • health informatics
  • clinical decision support system
  • federated learning

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Published Papers (1 paper)

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Research

31 pages, 1485 KB  
Article
Explainable Multi-Modal Medical Image Analysis Through Dual-Stream Multi-Feature Fusion and Class-Specific Selection
by Naeem Ullah, Ivanoe De Falco and Giovanna Sannino
AI 2026, 7(1), 30; https://doi.org/10.3390/ai7010030 - 16 Jan 2026
Cited by 3 | Viewed by 1158
Abstract
Effective and transparent medical diagnosis relies on accurate and interpretable classification of medical images across multiple modalities. This paper introduces an explainable multi-modal image analysis framework based on a dual-stream architecture that fuses handcrafted descriptors with deep features extracted from a custom MobileNet. [...] Read more.
Effective and transparent medical diagnosis relies on accurate and interpretable classification of medical images across multiple modalities. This paper introduces an explainable multi-modal image analysis framework based on a dual-stream architecture that fuses handcrafted descriptors with deep features extracted from a custom MobileNet. Handcrafted descriptors include frequency-domain and texture features, while deep features are summarized using 26 statistical metrics to enhance interpretability. In the fusion stage, complementary features are combined at both the feature and decision levels. Decision-level integration combines calibrated soft voting, weighted voting, and stacking ensembles with optimized classifiers, including decision trees, random forests, gradient boosting, and logistic regression. To further refine performance, a hybrid class-specific feature selection strategy is proposed, combining mutual information, recursive elimination, and random forest importance to select the most discriminative features for each class. This hybrid selection approach eliminates redundancy, improves computational efficiency, and ensures robust classification. Explainability is provided through Local Interpretable Model-Agnostic Explanations, which offer transparent details about the ensemble model’s predictions and link influential handcrafted features to clinically meaningful image characteristics. The framework is validated on three benchmark datasets, i.e., BTTypes (brain MRI), Ultrasound Breast Images, and ACRIMA Retinal Fundus Images, demonstrating generalizability across modalities (MRI, ultrasound, retinal fundus) and disease categories (brain tumor, breast cancer, glaucoma). Full article
(This article belongs to the Special Issue Digital Health: AI-Driven Personalized Healthcare and Applications)
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