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Novel Applications of Artificial Intelligence in Personalized Medicine
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Novel Applications of Artificial Intelligence in Personalized Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2004

Special Issue Editor

Dr. Aikaterini Sakagianni

E-Mail Website
Guest Editor
Intensive Care Unit, Sismanogleio General Hospital, 37 Sismanogleiou Str., 15126 Marousi, Greece
Interests: antibiotic resistance; antimicrobial resistance; intensive care unit; ICU; machine learning; predictive modeling; artificial intelligence; MI techniques; biostatistics and clinical bioinformatics.

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue on "Novel Applications of Artificial Intelligence in Personalized Medicine", which will be published in the Journal of Personalized Medicine. The intersection of artificial intelligence (AI) and personalized medicine has become a crucial area of research, offering transformative possibilities for individualized patient care. By leveraging AI's capabilities in data analysis, pattern recognition, and predictive modeling, healthcare is moving toward more precise, efficient, and patient-specific solutions.

This Special Issue aims to explore cutting-edge advancements in the application of AI to personalized medicine, aligning closely with the journal's mission to highlight innovative interdisciplinary research. The focus will be on groundbreaking approaches that integrate AI with healthcare to address critical challenges, from improving diagnostic accuracy to optimizing therapeutic strategies. This issue will provide a platform for researchers and practitioners to share insights and contribute to this rapidly evolving field.

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

  • AI-driven predictive modeling for personalized treatment;
  • Applications of AI in genomics and proteomics;
  • Development of AI tools for patient-specific drug discovery;
  • Ethical and practical challenges in AI-based healthcare;
  • AI in real-world clinical applications for personalized care.

We look forward to receiving your valuable contributions to this exciting Special Issue.

Dr. Aikaterini Sakagianni
Guest Editor

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. Journal of Personalized Medicine 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 2600 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 medicine 
  • predictive modeling 
  • genomics 
  • proteomics 
  • AI-driven diagnostics 
  • patient-specific treatment 
  • drug discovery 
  • healthcare innovation 
  • clinical applications of AI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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22 pages, 784 KB  
Article
From Coordination to Personalization: A Trust-Aware Simulation Framework for AI-Driven Personalized Decision Support in Emergency Departments
by Zoi Lygizou and Dimitris Kalles
J. Pers. Med. 2025, 15(12), 574; https://doi.org/10.3390/jpm15120574 - 28 Nov 2025
Viewed by 341
Abstract
Background/Objectives: Efficient and personalized task allocation in hospital emergency departments (EDs) is critical for operational efficiency and patient-centered care. However, the complexity of staff coordination and the variability among patients and healthcare professionals pose significant challenges. This study proposes a simulation-based framework [...] Read more.
Background/Objectives: Efficient and personalized task allocation in hospital emergency departments (EDs) is critical for operational efficiency and patient-centered care. However, the complexity of staff coordination and the variability among patients and healthcare professionals pose significant challenges. This study proposes a simulation-based framework for modeling doctors and nurses as intelligent agents guided by computational trust mechanisms. The objective is to explore how trust-informed coordination can support AI-driven and personalized decision-making in ED management. Methods: The framework was implemented in Unity, a 3D graphics platform, where agents assess their competence and patient-specific needs before undertaking tasks and adaptively coordinate with colleagues. The simulation environment enables real-time observation of workflow dynamics, resource utilization, and patient outcomes. We examined three scenarios—Baseline, Replacement, and Training—reflecting alternative staff management strategies. Results: Trust-informed task allocation balanced patient safety and efficiency by adaptively responding to nurse performance and patient acuity levels. In the Baseline scenario, prioritizing safety reduced errors but increased patient delays compared to a FIFO policy. The Replacement scenario improved throughput and reduced delays, though at additional staffing costs. The training scenario fostered long-term skill development among low-performing nurses, despite short-term delays and risks, supporting sustainable and personalized capacity building in ED teams. Conclusions: The proposed framework demonstrates the potential of computational trust for personalized and evidence-based decision support in emergency medicine. By linking staff coordination with adaptive and AI-informed decision-making, hospital managers are provided with a tool to evaluate alternative staffing and treatment policies under controlled and repeatable conditions. This work thus contributes to the broader vision of precision and personalized medicine, where operational decisions dynamically adapt to both patient needs and staff capabilities. Full article
(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Personalized Medicine)
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15 pages, 557 KB  
Article
Semi-Supervised Learning for Predicting Multiple Sclerosis
by Sotiris Kotsiantis, Georgia Melagraki, Vassilios Verykios, Aikaterini Sakagianni and John Matsoukas
J. Pers. Med. 2025, 15(5), 167; https://doi.org/10.3390/jpm15050167 - 24 Apr 2025
Viewed by 1218
Abstract
Background: Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system with a propensity to inflict severe neurological disability. Accurate and early prediction of MS progression is extremely crucial for its management and treatment. Methods: In this paper, [...] Read more.
Background: Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system with a propensity to inflict severe neurological disability. Accurate and early prediction of MS progression is extremely crucial for its management and treatment. Methods: In this paper, we compare a number of self-labeled semi-supervised learning methods used to predict MS from labeled and unlabeled medical data. Specifically, we compare the performance of Self-Training, SETRED, Co-Training, Co-Training by Committee, Democratic Co-Learning, RASCO, RelRASCO, CoForest, and TriTraining in different labeled ratios. The data contain clinical, imaging, and demographic features, allowing for a detailed comparison of each method’s predictive ability. Results and Conclusions: The experimental results demonstrate that several self-labeling semi-supervised learning (SSL) algorithms perform competitively in the task of Multiple Sclerosis (MS) prediction, even when trained on as little as 30–40% of the labeled data. Notably, Co-Training by Committee, CoForest, and TriTraining consistently deliver high performance across all metrics (accuracy, F1-score, and MCC). Full article
(This article belongs to the Special Issue Novel Applications of Artificial Intelligence in Personalized Medicine)
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