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The Roles of AI in Disease Diagnosis and Treatment
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The Roles of AI in Disease Diagnosis and Treatment

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1735

Special Issue Editor

Prof. Dr. Miguel Souto-Bayarri

E-Mail
Guest Editor
Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain
Interests: digital image processing; digital radiology; computer aided diagnosis (CAD); chest computed tomography imaging; cardiac magnetic resonance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen an increased presence of artificial intelligence (AI) in hospital settings, with AI-based programmes already in operation within several healthcare systems. Nevertheless, the role of AI in different medical specialties is highly diverse, and its full potential is yet to be realised. The future of AI in healthcare is promising, and its integration into medical practise is poised to transform the profession.

The present Special Issue aims to comprise the latest advances in the field of artificial intelligence (AI) and their potential applications in the diagnosis and treatment of different diseases. A particular focus will be given to radiomics and the integration of this field with clinical, genomic and proteomic data, with the objective of facilitating a deeper understanding of pathological mechanisms at the molecular level. The analysis of this information and its subsequent integration into daily clinical practise has the potential to transform the future of the medical profession.

The journal invites submissions of original research papers, review articles and short communications that focus on the applications of artificial intelligence in the diagnosis and treatment of diseases such as cancer and different neurological pathologies.

Prof. Dr. Miguel Souto-Bayarri
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 250 words) can be sent to the Editorial Office for assessment.

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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
  • radiomics
  • machine learning
  • deep learning
  • diagnosis
  • treatment
  • cancer

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Published Papers (2 papers)

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Research

11 pages, 3914 KB  
Article
Development of an Artificial Intelligence-Based Chromosome Interpretation System for Amniotic Fluid Karyotyping
by Kuan-Han Wu, Hsuan-Wei Huang, Chia Yun Lin, Hsu-Tung Huang, Tzuo-Yau Fan, Yueh-Peng Chen, Yung-Chiao Chang, Te-Yao Hsu and Kuo-Chung Lan
Int. J. Mol. Sci. 2026, 27(4), 1746; https://doi.org/10.3390/ijms27041746 - 11 Feb 2026
Viewed by 380
Abstract
Conventional G-banded karyotyping remains indispensable in prenatal diagnosis but continues to rely on labor-intensive, expertise-dependent visual examination. To address these challenges, we developed a modular artificial intelligence (AI) workflow that automates chromosome interpretation from amniotic fluid metaphase images. The system integrates image denoising, [...] Read more.
Conventional G-banded karyotyping remains indispensable in prenatal diagnosis but continues to rely on labor-intensive, expertise-dependent visual examination. To address these challenges, we developed a modular artificial intelligence (AI) workflow that automates chromosome interpretation from amniotic fluid metaphase images. The system integrates image denoising, chromosome segmentation, overlap screening, and morphology-based classification, and was trained using 13,223 clinical cases comprising more than 50,000 manually annotated chromosomes. Across training, temporal validation, and independent testing cohorts, classification accuracy remained consistently high (97.45%, 96.95%, and 95.72%, respectively). The overlap-recognition module further reduced downstream errors by reliably identifying composite chromosome regions. When applied to unsorted metaphase images from a later clinical cohort, the workflow successfully generated draft karyotypes without manual sorting and maintained close concordance with expert review. These findings demonstrate that an AI-assisted pipeline can support cytogenetic laboratories by streamlining the most labor-intensive steps of karyotyping, potentially enhancing diagnostic efficiency while preserving interpretive reliability. Full article
(This article belongs to the Special Issue The Roles of AI in Disease Diagnosis and Treatment)
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16 pages, 7215 KB  
Article
Integrating Multi-Source Directed Gene Networks and Multi-Omics Data to Identify Cancer Driver Genes Based on Graph Neural Networks
by Yuetong Jiang, Yunjiong Liu, Ruoyao Qi, Shaowei Li and Tianying Zhang
Int. J. Mol. Sci. 2025, 26(24), 12132; https://doi.org/10.3390/ijms262412132 - 17 Dec 2025
Viewed by 790
Abstract
Precisely identifying cancer drivers helps us to understand the molecular mechanisms of cancer, offering critical targets for early diagnosis. Despite the increasing application of graph neural networks in predicting cancer driver genes, existing approaches do not fully leverage the information from gene networks, [...] Read more.
Precisely identifying cancer drivers helps us to understand the molecular mechanisms of cancer, offering critical targets for early diagnosis. Despite the increasing application of graph neural networks in predicting cancer driver genes, existing approaches do not fully leverage the information from gene networks, and are unable to effectively extract node features from directed graphs. To this end, we propose MDIGNN, a novel deep learning model designed to identify cancer driver genes by integrating directed gene networks with multi-omics data. First, we construct a directed graph through the integration of existing gene networks from diverse databases and multi-omics data. Then, to encode the edge directionality, we develop a graph neural network based on the magnetic Laplacian, which relies on a complex Hermitian matrix for representing the directed graph structure. Next, we apply the channel attention and spatial attention mechanisms to improve the model’s feature representation ability. Finally, MDIGNN uses a fully connected layer to compute the cancer driver probability for each gene. In a comparative evaluation, MDIGNN outperforms existing state-of-the-art methods in the field, and it is capable of detecting potential cancer driver genes. Full article
(This article belongs to the Special Issue The Roles of AI in Disease Diagnosis and Treatment)
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