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Leveraging AI Algorithms to Enhance Healthcare Systems
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Leveraging AI Algorithms to Enhance Healthcare Systems

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: 10 July 2026 | Viewed by 2255

Special Issue Editors

Dr. Antonio Pesqueira

E-Mail Website
Guest Editor
ISCTE—University Institute of Lisbon, Lisbon, Portugal
Interests: digital technologies; healthcare data management
Dr. Andreia De Bem Machado

E-Mail Website
Guest Editor
Engineering and Knowledge Management Department, Federal University of Santa Catarina (UFSC), Florianópolis 88040-900, Brazil
Interests: innovation; hybrid education; healthcare; active methodologies; digital transformation; distance education; digital education; higher education; inclusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Leveraging AI Algorithms to Enhance Healthcare Systems”, will offer a comprehensive exploration of the transformative impact of artificial intelligence on modern medicine and healthcare delivery. It aims to bridge the gap between theoretical AI concepts and practical clinical applications, presenting a vital resource for a multidisciplinary audience. Its chapters will delve into the core AI and machine learning methodologies—from neural networks to natural language processing—that are revolutionizing medical diagnostics, predictive modeling for disease outbreaks, and the development of personalized treatment protocols.

Key sections will scrutinize the role of AI in interpreting complex medical imaging, accelerating drug discovery pipelines, and optimizing hospital operational workflows for enhanced efficiency and patient outcomes. Furthermore, this Special Issue will address the critical ethical, regulatory, and data privacy challenges inherent in integrating AI into clinical practice. By providing in-depth case studies and forward-looking perspectives, this issue aims to equip researchers, clinicians, data scientists, and healthcare administrators with the knowledge required to navigate and harness the power of AI, ultimately fostering a new era of data-driven, patient-centric healthcare.

Dr. Antonio Pesqueira
Dr. Andreia De Bem Machado
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. Systems 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 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

  • artificial intelligence
  • healthcare systems
  • machine learning
  • medical diagnostics
  • digital health

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Published Papers (2 papers)

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28 pages, 3358 KB  
Article
A Multi-Head Attention Soft Random Forest for Interpretable Patient No-Show Prediction
by Ninda Nurseha Amalina and Heungjo An
Systems 2026, 14(5), 576; https://doi.org/10.3390/systems14050576 - 19 May 2026
Viewed by 176
Abstract
Unattended scheduled appointments (“patient no-shows” henceforth) adversely affect healthcare providers and patients’ health, disrupting the continuity of care, operational efficiency, and allocation of medical resources. Therefore, accurate predictive modeling is needed to reduce the impact of patient no-shows. Although machine learning methods, such [...] Read more.
Unattended scheduled appointments (“patient no-shows” henceforth) adversely affect healthcare providers and patients’ health, disrupting the continuity of care, operational efficiency, and allocation of medical resources. Therefore, accurate predictive modeling is needed to reduce the impact of patient no-shows. Although machine learning methods, such as logistic regression, random forests, and decision trees, are widely used to predict patient no-shows, they often rely on hard decision splits and static feature importance, limiting adaptability to complex patient behaviors. To address this limitation, we propose a hybrid multi-head attention soft random forest (MHASRF) model that integrates attention mechanisms into a random forest using probabilistic soft splitting. It assigns attention weights across the trees, enabling attention on specific patient behaviors. The MHASRF model exhibited an accuracy of 88.24%, specificity of 91.21%, precision of 81.60%, recall of 82.01%, F1-score of 81.81%, and area under the receiver operating characteristic curve of 94.07%, demonstrating high and balanced performance across metrics. It could also identify key predictors of patient no-shows at two feature-importance levels (tree and attention mechanism), providing deeper insights into patient no-shows. Thus, the proposed MHASRF model is a robust, adaptable, and interpretable method for predicting patient no-shows that can help healthcare providers optimize resources. Full article
(This article belongs to the Special Issue Leveraging AI Algorithms to Enhance Healthcare Systems)
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30 pages, 2444 KB  
Systematic Review
The Decentralized AI Ecosystem in Healthcare: A Systematic Review of Technologies, Governance, and Implementation
by Antonio Pesqueira, Carmen Cucul, Thomas Egelhof, Stephanie Fuchs, Leilei Tang, Natalia Sofia and Andreia de Bem Machado
Systems 2026, 14(4), 414; https://doi.org/10.3390/systems14040414 - 9 Apr 2026
Viewed by 1488
Abstract
This research examines the emerging ecosystem of models that are developed and run across a distributed network of computers called decentralized artificial intelligence. The focus is to understand these models in the healthcare context and with a focus on their core components: technologies, [...] Read more.
This research examines the emerging ecosystem of models that are developed and run across a distributed network of computers called decentralized artificial intelligence. The focus is to understand these models in the healthcare context and with a focus on their core components: technologies, governance frameworks, and real-world applications. A systematic literature review was conducted, analyzing peer-reviewed studies from PubMed, Scopus, and Web of Science to map the current landscape of the field. The primary objective was to synthesize the current research on decentralized approaches in healthcare, including core approaches like federated learning and blockchain-based AI models, as well as emerging concepts such as agentic AI blockchain-based AI models and DAOs, to comprehend their application in clinical and operational settings. The research assesses the maturity of these implementations, ranging from pilot programs to large-scale organizational settings. It also identified the key computational and technical methods and platforms used and the key benefits and challenges influencing their adoption. The findings underscore the pivotal role of the decentralized paradigm in addressing the fundamental limitations of traditional AI, including data privacy, trust, institutional silos, and regulatory complexity. Insights are also offered for healthcare providers, technology developers, researchers, and policymakers aiming to navigate and leverage decentralized AI to build more equitable, efficient, and collaborative healthcare systems. Full article
(This article belongs to the Special Issue Leveraging AI Algorithms to Enhance Healthcare Systems)
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