Integrating Mathematical Modeling and Data Analysis in Personalized Medical Research

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 452

Special Issue Editors


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Department of Radiological Informatics and Statistics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; biophysics; mathematical modeling in biomedical and social sciences; health and biomedical informatics
Special Issues, Collections and Topics in MDPI journals

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Special Issue Information

Dear Colleagues,

This Special Issue invites authors to submit manuscripts to either

  • Journal of Personalized Medicine (ISSN 2075-4426) for topics related to personalized medicine.
  • Applied Sciences (ISSN 2076-3417) for articles addressing biosciences or bioengineering applications in human health and disease research.

The increasing prominence of mathematical modeling, computational methods, and data analysis in health sciences has attracted significant attention from both biomedical researchers and computer scientists. However, there is often a disconnect between these fields: computer scientists and experts in mathematical modeling may lack awareness of health problems that could benefit from their expertise, while medical researchers may not always recognize mathematics as a potential source of solutions to their challenges. This gap presents an opportunity to expand the application of computational techniques in health sciences, particularly in personalized medicine.

Future developments could focus on creating more accurate methods and introducing innovative approaches that reveal unexpected or previously unknown insights into pressing health issues. The goal of this Special Issue is to explore and expand the intersection of mathematics and health sciences, fostering collaboration and mutual enrichment between these fields, with a particular emphasis on personalized medicine.

We invite submissions to this joint Special Issue, including original research articles, reviews, or hybrid original-review papers. Topics of interest include the following:

  • Novel computational methods or algorithms in personalized medicine.
  • Discussions of the role and significance of mathematical approaches in addressing health-related challenges, with a focus on personalized healthcare.
  • Medical data analysis, including graphical and numerical data representations, tailored to individual patient profiles.
  • Applications of computational methods in various branches of medicine, bioinformatics, public health, physical education, or quality-of-life research, emphasizing personalized treatment strategies.

By integrating mathematical modeling and data processing with personalized medicine, this Special Issue aims to advance the development of individualized healthcare solutions.

You may choose our Joint Special Issue in Applied Sciences.

Prof. Dr. Dorota Bielińska-Wąż
Prof. Dr. Piotr Wąż
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. 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

  • personalized medicine
  • health informatics
  • mathematical modeling
  • data analysis
  • bioinformatics
  • artificial intelligence
  • machine learning

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Published Papers (2 papers)

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Research

23 pages, 1050 KiB  
Article
Quantitative Method for Monitoring Tumor Evolution During and After Therapy
by Paolo Castorina, Filippo Castiglione, Gianluca Ferini, Stefano Forte and Emanuele Martorana
J. Pers. Med. 2025, 15(7), 275; https://doi.org/10.3390/jpm15070275 - 28 Jun 2025
Viewed by 110
Abstract
Objectives: The quantitative analysis of tumor progression—monitored during and immediately after therapeutic interventions—can yield valuable insights into both long-term disease dynamics and treatment efficacy. Methods: We used a computational approach designed to support clinical decision-making, with a focus on personalized patient care, [...] Read more.
Objectives: The quantitative analysis of tumor progression—monitored during and immediately after therapeutic interventions—can yield valuable insights into both long-term disease dynamics and treatment efficacy. Methods: We used a computational approach designed to support clinical decision-making, with a focus on personalized patient care, based on modeling therapy effects using effective parameters of the Gompertz law. Results: The method is applied to data from in vivo models undergoing neoadjuvant chemoradiotherapy, as well as conventional and FLASH radiation treatments. Conclusions: This user-friendly, phenomenological model captures distinct phases of treatment response and identifies a critical dose threshold distinguishing complete response from partial response or tumor regrowth. These findings lay the groundwork for real-time quantitative monitoring of disease progression during therapy and contribute to a more tailored and predictive clinical strategy. Full article
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13 pages, 532 KiB  
Article
The Impact of AI-Driven Chatbot Assistance on Protocol Development and Clinical Research Engagement: An Implementation Report
by Kusal Weerasinghe, David B. Olawade, Jennifer Teke, Maines Msiska and Stergios Boussios
J. Pers. Med. 2025, 15(7), 269; https://doi.org/10.3390/jpm15070269 - 24 Jun 2025
Viewed by 208
Abstract
Background: The integration of artificial intelligence (AI) into healthcare research has the potential to enhance research capacity, streamline protocol development, and reduce barriers to engagement. Medway NHS Foundation Trust identified a plateau in homegrown research participation, particularly among clinicians with limited research experience. [...] Read more.
Background: The integration of artificial intelligence (AI) into healthcare research has the potential to enhance research capacity, streamline protocol development, and reduce barriers to engagement. Medway NHS Foundation Trust identified a plateau in homegrown research participation, particularly among clinicians with limited research experience. A generative AI-driven chatbot was introduced to assist researchers in protocol development by providing step-by-step guidance, prompting ethical and scientific considerations, and offering immediate feedback. Methods: The chatbot was developed using OpenAI’s GPT-3.5 architecture, customised with domain-specific training based on Trust guidelines, Health Research Authority (HRA) requirements, and Integrated Research Application System (IRAS) submission protocols. It was deployed to guide researchers through protocol planning, ensuring compliance with ethical and scientific standards. A mixed-methods evaluation was conducted using a qualitative-dominant sequential explanatory design. Seven early adopters completed a 10-item questionnaire (5-point Likert scales), followed by eight free-flowing interviews to achieve thematic saturation. Results: Since its launch, the chatbot has received an overall performance rating of 8.86/10 from the seven survey respondents. Users reported increased confidence in protocol development, reduced waiting times for expert review, and improved inclusivity in research participation across professional groups. However, limitations in usage due to free-tier platform constraints were identified as a key challenge. Conclusions: AI-driven chatbot tools show promise in supporting research engagement in busy clinical environments. Future improvements should focus on expanding access, optimising integration, and fostering collaboration among NHS institutions to enhance research efficiency and inclusivity. Full article
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