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Applied Sciences
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New Advances in Artificial Intelligence and Medical Data Science
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New Advances in Artificial Intelligence and Medical Data Science

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 3507

Special Issue Editors

Dr. Han Liu

E-Mail Website
Guest Editor
School of Software, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Hongyang Chen

E-Mail Website
Guest Editor
Zhejiang Lab, Hangzhou 310012, China
Interests: large language models; graph neural networks
Dr. Xiaotong Zhang

E-Mail Website
Guest Editor
School of Software, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence; data mining; graph neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence and medical data science is a dynamic and transformative field that leverages advanced computational techniques and algorithms to analyze and interpret vast quantities of medical data. Via the integration of AI with medical informatics, this discipline aims to revolutionize healthcare by providing enhanced diagnostic accuracy, personalized treatment plans, and improved patient outcomes. 

This Special Issue aims to explore recent research and developments regarding the application of AI in medical data science. It therefore welcomes the submission of papers that employ machine learning, deep learning, and large language models to extract meaningful insights from complex medical datasets, enabling more precise and efficient medical decision-making. The integration of AI into medical data science holds the potential to accelerate scientific discoveries, optimize clinical workflows, and deliver high-quality healthcare services to patients worldwide. 

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Machine learning and deep learning techniques for medical data analysis; 
  • Natural language processing in clinical notes and the medical literature; 
  • Large language models for medical data analysis; 
  • Predictive modeling and analytics in healthcare; 
  • AI-assisted diagnosis and treatment planning; 
  • Mobile health and wearable devices; 
  • Data privacy and security in healthcare AI applications.
  • Computer vision in medicine or healthcare;
  • Big data analytics in medical data;
  • Medical robotics, intelligent medical devices and smart technologies.

Dr. Han Liu
Dr. Hongyang Chen
Dr. Xiaotong Zhang
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. Applied Sciences is an international peer-reviewed open access semimonthly 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
  • machine learning
  • data mining
  • large language models
  • medical data

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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

17 pages, 2105 KiB  
Article
An Analysis of the Impact of Digital Therapeutic Interventions on Attention and Working Memory in Children with Attention-Deficit/Hyperactivity Disorder: A Randomized Controlled Trial
by Seon-Chil Kim and Hojun Lee
Appl. Sci. 2025, 15(2), 788; https://doi.org/10.3390/app15020788 - 15 Jan 2025
Viewed by 1880
Abstract
Previous research has investigated non-pharmacological digital therapeutic interventions to improve compliance and reduce side effects in attention-deficit/hyperactivity disorder (ADHD) medication treatments for children. This study focuses on validating the effects of game-based intervention content for enhancing working memory and concentration. It tracks quantitative [...] Read more.
Previous research has investigated non-pharmacological digital therapeutic interventions to improve compliance and reduce side effects in attention-deficit/hyperactivity disorder (ADHD) medication treatments for children. This study focuses on validating the effects of game-based intervention content for enhancing working memory and concentration. It tracks quantitative changes to evaluate improvements in concentration and working memory when digital game-based content is used as adjunct therapy alongside medication for children with ADHD. Thirty children participated; one group received digital therapeutic intervention based on game content alongside medication (experimental) and the other group received conventional treatments (control). The study results show that children with ADHD in the experimental group, who use digital game-based content, exhibit a reduction of 8.13 ± 6.71 points in the K-ARS total score at the fourth week compared to baseline, while the control group shows a reduction of 7.14 ± 8.73 points. Inattention decreases by 36.84% in the experimental group and 28.56% in the control group, while hyperactivity–impulsivity decreases by 50.71% in the experimental group and 34.00% in the control group. All the results are analyzed using a paired t-test between baseline and the fourth week. Significant decreases in the K-CBCL total problem behavior score and internalizing and externalizing behaviors are consistently observed at 28 days compared with baseline. The FAIR attention–concentration test results show significant differences between the experimental and control groups in the Q-percentile and Q-standard scores, with repeated measures ANOVA results showing p = 0.006 and p = 0.007, respectively. Digital content was shown to influence digital therapeutic intervention—a non-pharmacological treatment for ADHD. Full article
(This article belongs to the Special Issue New Advances in Artificial Intelligence and Medical Data Science)
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14 pages, 796 KiB  
Article
Use of Attention Maps to Enrich Discriminability in Deep Learning Prediction Models Using Longitudinal Data from Electronic Health Records
by Lucía A. Carrasco-Ribelles, Margarita Cabrera-Bean, Jose Llanes-Jurado and Concepción Violán
Appl. Sci. 2025, 15(1), 146; https://doi.org/10.3390/app15010146 - 27 Dec 2024
Viewed by 811
Abstract
Background: In predictive modelling, particularly in fields such as healthcare, the importance of understanding the model’s behaviour rivals, if not surpasses, that of discriminability. To this end, attention mechanisms have been included in deep learning models for years. However, when comparing different models, [...] Read more.
Background: In predictive modelling, particularly in fields such as healthcare, the importance of understanding the model’s behaviour rivals, if not surpasses, that of discriminability. To this end, attention mechanisms have been included in deep learning models for years. However, when comparing different models, the one with the best discriminability is usually chosen without considering the clinical plausibility of their predictions. Objective: In this work several attention-based deep learning architectures with increasing degrees of complexity were designed and compared aiming to study the balance between discriminability and plausibility with architecture complexity when working with longitudinal data from Electronic Health Records (EHRs). Methods: We developed four deep learning-based architectures with attention mechanisms that were progressively more complex to handle longitudinal data from EHRs. We evaluated their discriminability and resulting attention maps and compared them amongst architectures and different input processing approaches. We trained them on 10 years of data from EHRs from Catalonia (Spain) and evaluated them using a 5-fold cross-validation to predict 1-year all-cause mortality in a subsample of 500,000 people over 65 years of age. Results: Generally, the simplest architectures led to the best overall discriminability, slightly decreasing with complexity by up to 8.7%. However, the attention maps resulting from the simpler architectures were less informative and less clinically plausible compared to those from more complex architectures. Moreover, the latter could give attention weights both in the time and feature domains. Conclusions: Our results suggest that discriminability and more informative and clinically plausible attention maps do not always go together. Given the preferences within the healthcare field for enhanced explainability, establishing a balance with discriminability is imperative. Full article
(This article belongs to the Special Issue New Advances in Artificial Intelligence and Medical Data Science)
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