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Algorithms
Special Issues
AI-Assisted Medical Diagnostics
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AI-Assisted Medical Diagnostics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 464

Special Issue Editor

Dr. Milan Toma

E-Mail Website
Guest Editor
Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA
Interests: biomechanical engineering; computational mechanics; computational biomechanics; image processing; brain injuries; fetus injuries; impact biomechanics; cardiovascular fluid–structure interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue seeks to spotlight the transformative potential of artificial intelligence (AI) in the realm of medical diagnostics. We aim to showcase pioneering research at the intersection of AI-driven computational methods and clinical practice, emphasizing innovative solutions that advance diagnostic accuracy and healthcare delivery. The Issue will focus on how AI technologies (particularly machine learning and deep learning) can enhance medical diagnostics, support clinical decision-making, and improve patient outcomes.

We invite submissions addressing a broad range of topics such as predictive diagnostics, medical image interpretation and classification, and data-driven personalized diagnosis, underscoring the critical role of AI in tackling complex diagnostic challenges. Suitable contributions include original research articles, comprehensive reviews, and real-world applications that demonstrate the impact and promise of AI-assisted diagnostics in modern medicine.

Dr. Milan Toma
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. Algorithms 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 1800 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

  • machine learning in healthcare
  • medical diagnosis algorithms
  • healthcare technology innovation
  • predictive analytics in medicine
  • data-driven medical solutions
  • artificial intelligence in diagnostics
  • clinical decision support systems

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

Published Papers (1 paper)

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Research

23 pages, 2640 KiB  
Article
DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
by Lan Wei and Catherine Mooney
Algorithms 2025, 18(8), 486; https://doi.org/10.3390/a18080486 - 5 Aug 2025
Viewed by 285
Abstract
Electroencephalogram (EEG) analysis is essential for diagnosing neurological disorders but typically requires expert interpretation and significant time. Purpose: This study aims to automate the classification of normal and abnormal EEG recordings to support clinical diagnosis and reduce manual workload. Automating the initial screening [...] Read more.
Electroencephalogram (EEG) analysis is essential for diagnosing neurological disorders but typically requires expert interpretation and significant time. Purpose: This study aims to automate the classification of normal and abnormal EEG recordings to support clinical diagnosis and reduce manual workload. Automating the initial screening of EEGs can help clinicians quickly identify potential neurological abnormalities, enabling timely intervention and guiding further diagnostic and treatment strategies. Methodology: We utilized the Temple University Hospital EEG dataset to develop a DenseNet-based deep learning model. To enable a fair comparison of different EEG representations, we used three input types: signal images, spectrograms, and scalograms. To reduce dimensionality and simplify computation, we focused on two channels: T5 and O1. For interpretability, we applied Local Interpretable Model-agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the EEG regions influencing the model’s predictions. Key Findings: Among the input types, spectrogram-based representations achieved the highest classification accuracy, indicating that time-frequency features are especially effective for this task. The model demonstrated strong performance overall, and the integration of LIME and Grad-CAM provided transparent explanations of its decisions, enhancing interpretability. This approach offers a practical and interpretable solution for automated EEG screening, contributing to more efficient clinical workflows and better understanding of complex neurological conditions. Full article
(This article belongs to the Special Issue AI-Assisted Medical Diagnostics)
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