Applications of Artificial Intelligence and Non-Invasive Electrocardiograms in Disease Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 34

Special Issue Editors


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Guest Editor
3rd Cardiology Department, Cardiovascular Prevention and Digital Cardiology Lab Aristotle, Hippokration General Hospital, University of Thessaloniki, Thessaloniki, Greece
Interests: cardiovascular prevention; heart failure; sports cardiology; digital cardiology; mHealth; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Carver School of Medicine, Department of Internal Medicine-Cardiovascular Medicine, University of Iowa, Iowa City, IA 52242, USA
Interests: electrocardiology; artificial intelligence; clinical electrophysiology

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the application of artificial intelligence (AI) and non-invasive electrocardiograms (ECGs) in disease diagnosis. It will highlight recent advancements and innovative uses of AI algorithms in interpreting ECG signals, which have significantly enhanced accuracy and efficiency in diagnosing various cardiovascular conditions. By leveraging the power of machine learning and deep learning, researchers have developed sophisticated models capable of detecting anomalies in ECG readings, thereby aiding clinicians in making more informed decisions.

Furthermore, this Special Issue will explore the potential of non-invasive ECG techniques in conjunction with AI for diagnosing not only cardiovascular diseases but also other conditions that may manifest with ECG changes. The articles in this Special Issue will delve into the challenges and opportunities presented by this integration, emphasizing the importance of data quality, algorithm transparency, and ethical considerations in the deployment of these technologies. Overall, this Special Issue will underscore the transformative impact of AI and non-invasive ECGs in modern medical diagnosis, paving the way for more personalized and precise healthcare solutions.

Dr. Constantinos Bakogiannis
Guest Editor

Dr. Samuel L. Johnston
Guest Editor Assistant

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. Diagnostics 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 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

  • artificial intelligence (AI)
  • non-invasive electrocardiogram (ECG)
  • disease diagnosis
  • medical imaging analysis
  • machine learning
  • deep learning
  • cardiovascular diseases
  • pattern recognition
  • data analytics

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Published Papers

This special issue is now open for submission.
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