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Bioengineering
Special Issues
Applications of Artificial Intelligence for Medical Diagnosis
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Applications of Artificial Intelligence for Medical Diagnosis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 147

Special Issue Editors

Dr. Alessio Luschi

E-Mail Website
Guest Editor
Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
Interests: clinical engineering; biomedical engineering; natural language processing; generative AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pietro Rubegni

E-Mail Website
Guest Editor
Dermatology Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy
Interests: clinical dermatology; oncological dermatology; extracorporeal photochemotherapy

Special Issue Information

Dear Colleagues,

In recent years, healthcare has been significantly transformed by the integration of Artificial Intelligence (AI), especially in the area of medical diagnosis. Generative AI and other advanced models are enabling new approaches to interpreting clinical data, supporting decision-making, and personalizing treatment plans. These technologies enhance diagnostic accuracy, improve workflow efficiency, and reduce the burden on healthcare professionals. AI tools are increasingly being used to analyze medical images, predict disease progression, assist in triage, and generate clinical documentation, among many other applications. As interdisciplinary collaboration grows, the ability to turn complex health data into meaningful insights continues to expand. This Special Issue explores the most innovative applications of AI across the healthcare spectrum, with a focus on diagnostic tools, decision support systems, and responsible implementation practices. By highlighting both research advances and real-world applications, the issue aims to showcase how AI can improve patient outcomes, support physicians in their daily practice, and contribute to a more effective and responsive healthcare system.

Dr. Alessio Luschi
Prof. Dr. Pietro Rubegni
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. Bioengineering 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 2700 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

  • healthcare
  • medicine
  • artificial intelligence
  • deep learning
  • machine learning
  • generative AI

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Published Papers (1 paper)

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Research

24 pages, 40577 KiB  
Article
Analysis of Microbiome for AP and CRC Discrimination
by Alessio Rotelli, Ali Salman, Leandro Di Gloria, Giulia Nannini, Elena Niccolai, Alessio Luschi, Amedeo Amedei and Ernesto Iadanza
Bioengineering 2025, 12(7), 713; https://doi.org/10.3390/bioengineering12070713 - 29 Jun 2025
Viewed by 57
Abstract
Microbiome data analysis is essential for understanding the role of microbial communities in human health. However, limited data availability often hinders research progress, and synthetic data generation could offer a promising solution to this problem. This study aims to explore the use of [...] Read more.
Microbiome data analysis is essential for understanding the role of microbial communities in human health. However, limited data availability often hinders research progress, and synthetic data generation could offer a promising solution to this problem. This study aims to explore the use of machine learning (ML) to enrich an unbalanced dataset consisting of microbial operational taxonomic unit (OTU) counts of 148 samples, belonging to 61 patients. In detail, 34 samples are from 16 adenomatous polyps (AP) patients, while 114 samples are from 46 colorectal cancer (CRC) patients. Synthesis of AP and CRC samples was conducted using the Synthetic Data Vault Python library, employing a Gaussian Copula synthesiser. Subsequently, the synthesised data quality was evaluated using a logistic regression model in parallel with an optimised support vector machine algorithm (polynomial kernel). The data quality is considered good when neither of the two algorithms can discriminate between real and synthetic data, showing low accuracy, F1 score, and precision values. Furthermore, additional statistical tests were employed to confirm the similarity between real and synthetic data. After data validation, layer-wise relevance propagation (LRP) was performed on a deep learning classifier to extract important OTU features from the generated dataset, to discriminate between CRC patients and those affected by AP. Exploiting the acquired features, which correspond to unique bacterial taxa, ML classifiers were trained and tested to estimate the validity of such microorganisms in recognising AP and CRC samples. The simplified version of the original OTU table opens up opportunities for further investigations, especially in the realm of extensive data synthesis. This involves a deeper exploration and augmentation of the condensed data to uncover new insights and patterns that might not be readily apparent in the original, more complex form. Digging deeper into the simplified data may help us better grasp the biological or ecological processes reflected in the OTU data. Transitioning from this exploration, the synergy of ML and synthetic data enrichment holds promise for advancing microbiome research. This approach enhances classification accuracy and reveals hidden microbial markers that could prove valuable in clinical practice as a diagnostic and prognostic tool. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence for Medical Diagnosis)
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