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Applied Sciences
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Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition
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Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 821

Special Issue Editors

Dr. Giorgio De Nunzio

E-Mail Website
Guest Editor
Department of Mathematics and Physics, University of Salento, and DReAM (Laboratorio Diffuso di Ricerca Interdisciplinare Applicata alla Medicina), 73100 Lecce, Italy
Interests: physics applied to medicine; radiomics; computer-assisted detection/diagnosis; machine/deep learning; artificial neural networks; artificial intelligence; omics sciences; precision medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Luana Conte

E-Mail Website
Guest Editor
Department of Mathematics and Physics, University of Salento, and DReAM (Laboratorio Diffuso di Ricerca Interdisciplinare Applicata alla Medicina), 73100 Lecce, Italy
Interests: artificial intelligence in medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a result of its rapid expansion, artificial intelligence (AI) is becoming a powerful tool serving numerous fields, including medicine. Its applications range from diagnostics to surgery, from drug development to rehabilitation, and from remote monitoring to patient assistance, continuing to grow exponentially.

Indeed, artificial intelligence in the medical field is now conceived as an aid to modern medicine. It is precisely in this scenario that technological tools and software used in the medical field are undergoing radical changes, with strong innovations to enable progressively early advanced diagnoses, increasingly personalized therapies, and to improve patients’ experience in general.

In the era of big data and omics sciences, global healthcare is in fact trying to move beyond the historical "one-size-fits-all" medical approach to embrace an increasingly personalized approach uniquely designed specifically for the patient, adopting each person's individual differences in genotype, environment, and lifestyle.

In recent years, there have been particularly tremendous advances in the applications of AI in a variety of omics studies, including genomics, transcriptomics, proteomics, metabolomics, radiomics, etc., and all multi-omics integration approaches. It is therefore highly timely to discuss the potential impact of the insights generated by new machine learning (ML) and deep learning (DL) technologies on medical support, clinical decisions, clinical research, the pharmaceutical industry, and the entire patient pathway, which seeks to be as personalized as possible.

From another perspective, large language models (LLMs), based on DL and trained on huge amounts of text data, allow for the generation of new information close to human responses, with the goal of producing virtual assistants and chatbots that provide personalized patient support, answer medical queries, schedule appointments, and offer basic triage services.

The goal of this Special Issue is therefore to collate articles highlighting the new opportunities, challenges, and perspectives of AI tools within precision medicine.

Both theoretical and experimental and case studies are welcome.

Dr. Giorgio De Nunzio
Dr. Luana Conte
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. 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

  • artificial intelligence
  • machine learning
  • deep learning
  • omic sciences
  • precision medicine
  • personalized medicine
  • genomics
  • proteomics
  • metabolomics
  • radiomics
  • radiogenomics
  • robotic surgery
  • assisting technologies
  • health monitoring
  • computer-assisted detection/diagnosis
  • chatbots
  • medical imaging
  • disease prediction
  • prognostics
  • drug discovery

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Published Papers (2 papers)

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Research

15 pages, 3326 KiB  
Article
Radiomics and Machine Learning Approaches for the Preoperative Classification of In Situ vs. Invasive Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE–MRI)
by Luana Conte, Rocco Rizzo, Alessandra Sallustio, Eleonora Maggiulli, Mariangela Capodieci, Francesco Tramacere, Alessandra Castelluccia, Giuseppe Raso, Ugo De Giorgi, Raffaella Massafra, Maurizio Portaluri, Donato Cascio and Giorgio De Nunzio
Appl. Sci. 2025, 15(14), 7999; https://doi.org/10.3390/app15147999 - 18 Jul 2025
Viewed by 265
Abstract
Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. [...] Read more.
Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE–MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE–MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE–MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)
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14 pages, 1520 KiB  
Article
Machine Learning-Based Detection of Cognitive Impairment from Eye-Tracking in Smooth Pursuit Tasks
by Vida Groznik, Andrea De Gobbis, Dejan Georgiev, Aleš Semeja and Aleksander Sadikov
Appl. Sci. 2025, 15(14), 7785; https://doi.org/10.3390/app15147785 - 11 Jul 2025
Viewed by 292
Abstract
Mild cognitive impairment represents a transitional phase between healthy ageing and dementia, including Alzheimer’s disease. Early detection is essential for timely clinical intervention. This study explores the viability of smooth pursuit eye movements (SPEM) as a non-invasive biomarker for cognitive impairment. A total [...] Read more.
Mild cognitive impairment represents a transitional phase between healthy ageing and dementia, including Alzheimer’s disease. Early detection is essential for timely clinical intervention. This study explores the viability of smooth pursuit eye movements (SPEM) as a non-invasive biomarker for cognitive impairment. A total of 115 participants—62 with cognitive impairment and 53 cognitively healthy controls—underwent comprehensive neuropsychological assessments followed by an eye-tracking task involving smooth pursuit of horizontally and vertically moving stimuli at three different speeds. Quantitative metrics such as tracking accuracy were extracted from the eye movement recordings. These features were used to train machine learning models to distinguish cognitively impaired individuals from controls. The best-performing model achieved an area under the ROC curve (AUC) of approximately 68 %, suggesting that SPEM-based assessment has potential as part of an ensemble of eye-tracking based screening methods for early cognitive decline. Of course, additional paradigms or task designs are required to enhance diagnostic performance. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)
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