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Information
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Medical Data Visualization
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Medical Data Visualization

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2883

Special Issue Editors

Dr. Shuo-Chen Chien

E-Mail Website
Guest Editor
Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 11031, Taiwan
Interests: artificial intelligence; data visualization; natural language processing; CDSS alert system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wen-Shan Jian

E-Mail Website
Guest Editor
School of Health Care Administration, Taipei Medical University, Taipei 11031, Taiwan
Interests: Internet of Things; healthcare management; artificial intelligence; data visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the dynamic field of data science, the ability to effectively visualize information stands as a fundamental pillar, particularly within the medical domain. This Special Issue is devoted to exploring cutting-edge advancements in the visualization of complex medical data—a critical component as these datasets grow in both size and complexity. Effective visualization is essential not only for enhancing clinical decision making and research but also for improving patient outcomes.

We invite submissions of original research articles, insightful case studies, and comprehensive review papers that address both challenges and innovations in medical data visualization. This Special Issue will cover a wide range of topics, including but not limited to the following:

  • Innovative visualization techniques: development and implementation of advanced tools and software designed to manage large-scale medical datasets.
  • Interactive tools for medical data analysis: emphasis on tools that support dynamic interaction with data to aid diagnosis, treatment planning, and outcome prediction.
  • Integration of multi-source data: strategies for visually integrating diverse data sources such as electronic health records, imaging data, and genomic information.
  • Ensuring patient data privacy and security: visualization approaches that maintain the integrity and confidentiality of patient data.
  • User-centred design in medical visualization: approaches focusing on usability and accessibility, catering to various user groups, including clinicians, researchers, and patients.
  • Virtual and augmented reality applications in medicine: use of VR and AR technologies for immersive visualization in medical training, surgical planning, and patient education.
  • Case studies and evaluation of visualization tools: documentation of real-world applications and assessments of visualization tools in clinical settings.
  • Scalability and performance optimization: exploration of methods to enhance the performance and scalability of visualization tools, ensuring the efficient handling of large and complex datasets without sacrificing speed or user experience.
  • Visualization for long-term care management: development of visualization tools aimed at improving monitoring, management, and planning in long-term care, enhancing the understanding of patient progress, resource allocation, and coordinated care.
  • Predictive analytics in visualization: advanced visualization techniques that integrate predictive analytics for forecasting medical conditions or outcomes, such as disease spread, patient deterioration, or treatment responses based on historical and real-time data.

Dr. Shuo-Chen Chien
Prof. Dr. Wen-Shan Jian
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. Information 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 1600 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

  • advanced visualization techniques
  • large-scale data management
  • interactive data analysis tools
  • diagnostic visualization tools
  • visualization of treatment planning
  • visualization of multi-source data
  • visualization of electronic health records
  • integration of medical imaging data
  • visualization of genomic data
  • data privacy in healthcare
  • secure visualization of medical data
  • user-centered design in healthcare
  • accessibility in medical tools
  • virtual reality in medical training
  • augmented reality in surgical planning
  • real-world applications of visualization
  • clinical visualization tools
  • visualization tool scalability
  • high-performance visualization systems
  • visualization of long-term care
  • visualization tools for patient monitoring
  • resource allocation in healthcare
  • predictive analytics in medicine
  • forecasting in medical visualization
  • outcome prediction in healthcare

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

Published Papers (2 papers)

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Research

12 pages, 3677 KiB  
Article
Study on Radiation Protection Educational Tool Using Real-Time Scattering Radiation Distribution Calculation Method with Ray Tracing Technology
by Toshioh Fujibuchi
Information 2025, 16(4), 266; https://doi.org/10.3390/info16040266 - 26 Mar 2025
Viewed by 189
Abstract
In this study, we developed an application for radiation protection that calculates in real time the distribution of scattered radiation during fluoroscopy using ray tracing technology, assuming that most of the scattered radiation in the room originates from the patient and that the [...] Read more.
In this study, we developed an application for radiation protection that calculates in real time the distribution of scattered radiation during fluoroscopy using ray tracing technology, assuming that most of the scattered radiation in the room originates from the patient and that the scattered radiation originating from the patient travels linearly. The directional vectors and energy information for the scattered radiation spreading from the patient’s body surface to the outside of the body were obtained via simulation in a virtual X-ray fluoroscopy room. Based on this information, the scattered dose distribution in the X-ray room was calculated. The ratio of the scattered doses calculated by the method to those obtained from the Monte Carlo simulation was mostly within the range of 0.7 to 1.8 times, except for behind the X-ray machine. The scattered radiation distribution changed smoothly as the radiation protective plates were moved. When using protection plates with a high degree of freedom in their placement, it is not practical to measure the scattered radiation distribution each time. This application cannot be used for dose estimation for medical staff in clinical settings because it does not take into account the scattered radiation of non-patients and its dose calculation accuracy is low. However, the simple confirmation of the scattered radiation distribution and changes in staff dose led to an intuitive understanding of the appropriate placement of the protection plates. Full article
(This article belongs to the Special Issue Medical Data Visualization)
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31 pages, 6140 KiB  
Article
Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights
by Raza Hasan, Vishal Dattana, Salman Mahmood and Saqib Hussain
Information 2025, 16(1), 7; https://doi.org/10.3390/info16010007 - 26 Dec 2024
Cited by 2 | Viewed by 2133
Abstract
Diabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. Using the Pima Indian Diabetes dataset, [...] Read more.
Diabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. Using the Pima Indian Diabetes dataset, we developed an ensemble model with 85.01% accuracy leveraging AutoGluon’s AutoML framework. To address the “black-box” nature of machine learning, we applied XAI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Integrated Gradients (IG), Attention Mechanism (AM), and Counterfactual Analysis (CA), providing both global and patient-specific insights into critical risk factors such as glucose and BMI. These methods enable transparent and actionable predictions, supporting clinical decision-making. An interactive Streamlit application was developed to allow clinicians to explore feature importance and test hypothetical scenarios. Cross-validation confirmed the model’s robust performance across diverse datasets. This study demonstrates the integration of AutoML with XAI as a pathway to achieving accurate, interpretable models that foster transparency and trust while supporting actionable clinical decisions. Full article
(This article belongs to the Special Issue Medical Data Visualization)
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