applsci-logo

Journal Browser

Journal Browser

Applications of Artificial Intelligence in Biomedical Data Analysis and Health Informatics

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 1097

Special Issue Editor


E-Mail Website
Guest Editor
Department of Entertainment Industry, Vilnius Gediminas Technical University, Vilnius, Lithuania
Interests: public health; healthy lifestyle; healthcare; psychomotor competencies; education; security; MCDM

Special Issue Information

Dear Colleagues,

With its empowering effects on data processing and analysis, as well as its use for patient care and medical decision-making across the whole spectrum of health issues, artificial intelligence (AI) has emerged as an important enabler of biomedical data analysis and health informatics. Machines, particularly computing systems that use multicriteria decision procedures, are examples of artificial intelligence in its broadest sense. This field investigates how these computers can identify a variety of known and unknown conditions, as well as how they use the resources at their disposal to make judgments or learn how to operate in ways that are likely to improve the attainment of predetermined objectives. AI offloads a lot of work at a remarkable level of efficiency and speed in some of the less important aspects of healthcare activities, such as the evaluation, distinction, prevention, care, therapy, and administration of duties. AI has previously improved the pace of these medications, improved diagnostics, and produced even better results for particular patients by solving a complex array of biological data. The use of AI in healthcare systems around the world has been found to improve the overall treatment a patient receives and research services. However, there are still more questions than answers regarding ethical and regulatory issues.

Potential topics include, but are not limited to, the following:

  • Medical diagnosing and imaging (AI-based approaches to analyze images and other parameters from patients);
  • Genomics and personalized medicine (large-scale genomic data in AI);
  • Drug discovery and development (by using AI in the data analysis of a large data set, it makes it easy to dictate the effectiveness of the drugs in the treatment of certain diseases);
  • Disease outbreak analysis for indications (application of big data), predictive analytics to forecast disease outbreaks using data from social media, travel history, and patient records captured in real time;
  • Natural language processing (NLP) for health records (AI in the process and analysis of unstructured data in electronic health records (EHRs)—the data provided include doctors’ notes, patient histories, lab results, etc.;
  • M-Health Solutions and Telemedicine Services (an algorithm that is used in fit wearables) and distant monitoring systems to process such real-time health information;
  • Robots in operations (use of artificial intelligence in machines to learn algorithms that process data in real time while robotic surgeons operate);
  • Regulations that would concern AI in the realm of biological data and analytics, as well as biomedical informatics (chaining AI when it is continuously learning, reducing the bias of algorithms, international data transfers, the protection of patient information, etc.);
  • Issues of bias and social injustice in AI solutions (accountability, the integration of data gathering methods, AI fairness checks, and AI government regulation);
  • Real-life issues concerning AI and bioinformatics, including privacy and security issues such as meeting legal requirements, the profitable incorporation of new technologies, measures put in place to prevent the leakage of data, and more.

We support original research that focuses on new scientific advancements, as well as review and comparative publications.

Prof. Dr. Stanislavas Dadelo
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 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

  • diagnostic
  • personalization
  • predictive analytics
  • health records
  • drugs
  • remote monitoring
  • robotics
  • laws and rules
  • bias and social inequality
  • security

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

25 pages, 5837 KiB  
Article
Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
by Fatimah Alzahrani, Steve Maddock and Heidi Christensen
Appl. Sci. 2025, 15(11), 6267; https://doi.org/10.3390/app15116267 - 3 Jun 2025
Viewed by 388
Abstract
The development of automatic methods for early cognitive impairment (CI) detection has a crucial role to play in helping people obtain suitable treatment and care. Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink [...] Read more.
The development of automatic methods for early cognitive impairment (CI) detection has a crucial role to play in helping people obtain suitable treatment and care. Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink rate (EBR), head turn rate (HTR), and head movement statistical features (HMSFs)) for distinguishing between neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). Following prior work, we improve the multiple thresholds (MTs) approach specifically for EBR calculation to enhance performance and robustness, while the HTR and HMSFs are extracted using methods from previous work. The EBR, HTR, and HMSFs are evaluated using an in-the-wild video dataset captured in challenging environments. This method leverages clinically validated cues and automatically extracts features to enable classification. Experiments show that the proposed approach achieves competitive performance in distinguishing between ND, MCI, FMD, and HCs on in-the-wild datasets, with results comparable to audiovisual-based methods conducted in a lab-controlled environment. The findings highlight the potential of visual-based approaches to complement existing diagnostic tools and provide an efficient home-based monitoring system. This work advances the field by addressing traditional limitations and offering a scalable, cost-effective solution for early detection. Full article
Show Figures

Figure 1

22 pages, 3218 KiB  
Article
Dynamic Handwriting Features for Cognitive Assessment in Inflammatory Demyelinating Diseases: A Machine Learning Study
by Jiali Yang, Chaowei Yuan, Yiqiao Chai, Yukun Song, Shuning Zhang, Junhui Li, Mingying Lan and Li Gao
Appl. Sci. 2025, 15(11), 6257; https://doi.org/10.3390/app15116257 - 2 Jun 2025
Viewed by 379
Abstract
Cognitive impairment is common but often overlooked in patients with inflammatory demyelinating diseases such as multiple sclerosis and neuromyelitis optica spectrum disorder. The conventional assessments may fail to detect subtle deficits and require substantial time and expertise. We collected neuropsychological scores and real-time [...] Read more.
Cognitive impairment is common but often overlooked in patients with inflammatory demyelinating diseases such as multiple sclerosis and neuromyelitis optica spectrum disorder. The conventional assessments may fail to detect subtle deficits and require substantial time and expertise. We collected neuropsychological scores and real-time handwriting data across nine drawing tasks and tasks from the Symbol Digit Modalities Test in 93 patients. Temporal, pressure, and kinematic features were extracted, and machine learning classifiers were trained using five-fold cross-validation with bootstrap confidence intervals. The response timing and pen pressure metrics correlated significantly with global cognitive scores (|r| = 0.30–0.37, p < 0.01). A support vector machine using eight selected features achieved an area under the receiver-operating characteristic curve (AUC) of 0.910, and a streamlined five-feature variant maintained an equivalent performance (AUC = 0.921) while reducing the assessment time by 35%. These results indicate that digital handwriting metrics can complement the standard screening by capturing fine motor and temporal characteristics overlooked in conventional testing. Validation in larger, disease-balanced, and longitudinal cohorts is needed to confirm their clinical utility. Full article
Show Figures

Figure 1

Back to TopTop