Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

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

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 July 2026 | Viewed by 4814

Share This Special Issue

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

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 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. 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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

18 pages, 2905 KB

Open AccessArticle

Timestamp Supervision for Surgical Phase Recognition Using Semi-Supervised Deep Learning

by Julia de Enciso García, Alba Centeno López, Ángela González-Cebrián, María Paz Sesmero, Araceli Sanchis, Igor Paredes, Alfonso Lagares and Paula de Toledo

Appl. Sci. 2025, 15(23), 12525; https://doi.org/10.3390/app152312525 - 26 Nov 2025

Viewed by 104

Abstract

Surgical Phase Recognition (SPR) enables real-time, context-aware assistance during surgery, but its use remains limited by the cost and effort of dense video annotation. This study presents a Semi-Supervised Deep Learning framework for SPR in endoscopic pituitary surgery, aiming to reduce annotation requirements while maintaining performance. A Timestamp Supervision strategy is employed, where only one or two representative frames per phase are labeled. These labels are then propagated, creating pseudo-labels for unlabeled frames using an Uncertainty-Aware Temporal Diffusion (UATD) approach, based on confidence and temporal consistency. Multiple spatial and temporal architectures are evaluated on the PituPhase–SurgeryAI dataset, the largest publicly available collection of endoscopic pituitary surgeries to date, which includes an outside-the-body phase. Despite using less than 3% of the annotated data, the proposed method achieves an F1-score of 0.60 [0.55–0.65], demonstrating competitive performance against previous Supervised approaches in the same context. Removing the recurrent outside-the-body phase reduces misclassification and improves temporal consistency. These results demonstrate that uncertainty-guided Semi-Supervision is a scalable and clinically viable alternative to fully Supervised Learning for surgical workflow analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

► Show Figures

Figure 1

18 pages, 505 KB

Open AccessArticle

Augmenting, Not Replacing: Clinicians’ Perspectives on AI Adoption in Healthcare

by Francesco Sensi, Francesca Lizzi, Andrea Chincarini, Chiara Binelli, Laura Sartori and Alessandra Retico

Appl. Sci. 2025, 15(23), 12405; https://doi.org/10.3390/app152312405 - 22 Nov 2025

Viewed by 302

Abstract

Artificial intelligence (AI) is widely expected to transform healthcare, yet its adoption in clinical practice remains limited. This paper examines the perspectives of Italian clinicians and medical physicists on the drivers of and barriers to AI use. Using an online survey of healthcare professionals across different domains, we find that efficiency gains—such as reducing repetitive tasks and accelerating diagnostics—are the strongest incentives for adoption. However, trust in AI systems, explainability, and the limited availability of AI tools are major obstacles. Respondents emphasized that AI should augment, not replace, medical expertise, calling for participatory development processes where clinicians are actively involved in the design and validation of decision support tools. At the organizational level, the adoption of AI tools is facilitated by innovation-oriented leadership and sufficient resources, while conservative management and economic constraints hinder implementation. The awareness of regulatory frameworks, including the EU AI Act, is moderate, and many clinicians express the need for targeted training to support safe integration. Our findings suggest that the successful adoption of AI in healthcare will depend on building trust through transparency, clarifying legal responsibilities, and fostering organizational cultures that support collaboration between humans and AI. The role of AI in medicine is therefore best understood as a complement to clinical judgment, rather than a replacement. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

► Show Figures

Figure 1

15 pages, 1140 KB

Open AccessArticle

Deep Learning Framework for Facial Reconstruction Outcome Prediction: Integrating Image Inpainting and Depth Estimation for Computer-Assisted Surgical Planning

by Fabiano Bini, Guido Manni and Franco Marinozzi

Appl. Sci. 2025, 15(23), 12376; https://doi.org/10.3390/app152312376 - 21 Nov 2025

Viewed by 228

Abstract

Facial reconstructive surgery requires precise preoperative planning to optimize functional and aesthetic outcomes, but current imaging technologies like CT and MRI do not offer visualization of expected post-surgical appearance, limiting surgical planning capabilities. We developed a deep learning framework integrating facial inpainting and monocular depth estimation models to predict surgical outcomes and enable 2D and 3D planning from clinical photographs. Three state-of-the-art inpainting architectures (LaMa, LGNet, MAT) and three monocular depth estimation approaches (ZoeDepth, Depth Anything V2, DepthPro) were evaluated using the FFHQ dataset for inpainting and C3I-SynFace dataset for depth estimation, with comprehensive quantitative metrics assessing reconstruction quality and depth accuracy. For anatomically specific facial features, LGNet demonstrated superior performance across eyebrows (PSNR: 25.11, SSIM: 0.75), eyes (PSNR: 20.08, SSIM: 0.53), nose (PSNR: 25.70, SSIM: 0.88), and mouth (PSNR: 22.39, SSIM: 0.75), with statistically significant differences confirmed by paired t-tests (p < 0.001) and large effect sizes (Cohen’s d = 2.25–6.33). DepthPro significantly outperformed competing depth estimation models with absolute relative difference of 0.1426 (78% improvement over Depth Anything V2: 0.6453 and ZoeDepth: 0.6509) and δ₁ accuracy of 0.8373 (versus 0.6697 and 0.5271 respectively). This novel framework addresses a critical gap in surgical planning by providing comprehensive preoperative visualization of potential outcomes from standard clinical photographs, supporting applications from maxillofacial reconstruction to orbital and nasal procedures. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

► Show Figures

Figure 1

18 pages, 912 KB

Open AccessArticle

Artificial Intelligence in Medicine and Healthcare: A Complexity-Based Framework for Model–Context–Relation Alignment

by Emanuele Di Vita, Giovanni Caivano, Fabio Massimo Sciarra, Simone Lo Bianco, Pietro Messina, Enzo Maria Cumbo, Luigi Caradonna, Salvatore Nigliaccio, Davide Alessio Fontana, Antonio Scardina and Giuseppe Alessandro Scardina

Appl. Sci. 2025, 15(22), 12005; https://doi.org/10.3390/app152212005 - 12 Nov 2025

Viewed by 386

Abstract

Artificial intelligence (AI) is profoundly transforming medicine and healthcare, evolving from analytical tools aimed at automating specific tasks to integrated components of complex socio-technical systems. This work presents a conceptual and theoretical review proposing the Model–Context–Relation (M–C–R) framework to interpret how the effectiveness of Artificial Intelligence (AI) in medicine and healthcare emerges from the dynamic alignment among algorithmic, contextual, and relational dimensions. No new patient-level data were generated or analyzed. Through a qualitative conceptual framework analysis, the study integrates theoretical, regulatory, and applicative perspectives, drawing on the Revision of the Semiological Paradigm developed by the Palermo School, as well as on major international guidelines (WHO, European AI Act, FDA). The results indicate that AI-supported processes have been reported in the literature to improve clinical accuracy and workflow efficiency when appropriately integrated, yet its value depends on contextual adaptation and human supervision rather than on algorithmic performance alone. When properly integrated, AI functions as a digital semiotic extension of clinical reasoning and may enhance the physician’s interpretative capacity without replacing it. The M–C–R framework enables understanding of how performance, ethical reliability, and organizational sustainability emerge from the alignment between the technical model, the context of use, and relational trust. In this perspective, AI is conceptualized not as a decision-maker but as an adaptive cognitive partner, fostering a reflective, transparent, and person-centered medicine. The proposed approach supports the design of sustainable and ethically responsible AI systems within a Medicine of Complexity, in which human and artificial intelligence co-evolve to strengthen knowledge, accountability, and equity in healthcare systems. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

► Show Figures

Figure 1

15 pages, 2942 KB

Open AccessArticle

Development and Evaluation of a Next-Generation Medication Safety Support System Based on AI and Mixed Reality: A Study from South Korea

by Nathan Lucien Vieira, Su Jin Kim, Sangah Ahn, Ji Sim Yoon, Sook Hyun Park, Jeong Hee Hong, Min-Jeoung Kang, Il Kim, Meong Hi Son, Won Chul Cha and Junsang Yoo

Appl. Sci. 2025, 15(22), 12002; https://doi.org/10.3390/app152212002 - 12 Nov 2025

Viewed by 602

Abstract

Medication errors pose a significant threat to patient safety. Although Bar-Code Medication Administration (BCMA) has reduced error rates, it is constrained by handheld devices, workflow interruptions, and incomplete safeguards against wrong patients, wrong doses, or drug incompatibility. In this study, we developed and evaluated a next-generation BCMA system by integrating artificial intelligence and mixed reality technologies for real-time safety checks: Optical Character Recognition verifies medication–label concordance, facial recognition confirms patient identity, and a rules engine evaluates drug–diluent compatibility. Computer vision models achieved high recognition accuracy for drug vials (100%), medication labels (90%), QR codes (90%), and patient faces (90%), with slightly lower performance for intravenous fluids (80%). A mixed-methods evaluation was conducted in a simulated environment using the System Usability Scale (SUS), Reduced Instructional Materials Motivation Survey (RIMMS), Virtual Reality Sickness Questionnaire (VRSQ), and NASA Task Load Index (NASA-TLX). The results indicated excellent usability (median SUS = 82.5/100), strong user motivation (RIMMS = 3.7/5), minimal cybersickness (VRSQ = 0.4/6), and manageable cognitive workload (NASA-TLX = 31.7/100). Qualitative analysis highlighted the system’s potential to streamline workflow and serve as a digital “second verifier.” These findings suggest strong potential for clinical integration, enhancing medication safety at the point of care. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

► Show Figures

Figure 1

14 pages, 1430 KB

Open AccessArticle

Ensemble-Based Refinement of Landmark Annotations for DNA Ploidy Analysis in Digital Pathology

by Viktor Zoltán Jónás, Dániel Küttel, Béla Molnár and Miklós Kozlovszky

Appl. Sci. 2025, 15(22), 11892; https://doi.org/10.3390/app152211892 - 8 Nov 2025

Viewed by 238

Abstract

Reliable evaluation of image segmentation algorithms in digital pathology depends on high-quality annotation datasets. Landmark-type annotations, essential for cell-counting analyses, are often limited in quality or quantity for segmentation benchmarking, particularly in rare assays where annotation is scarce and costly. In this study, we investigate whether ensemble-inspired refinement of landmark annotations can improve the robustness of segmentation evaluation. Using 15 fluorescently imaged blood samples with more than 20,000 manually placed annotations, we compared three segmentation algorithms—a threshold-based method with clump splitting, a difference-of-Gaussians (DoG) approach, and a convolutional neural network (StarDist)—and used their combined outputs to generate an ensemble-derived ground truth. Confusion matrices and standard metrics (F1 score, precision, and sensitivity) were computed against both manual and ensemble-derived ground truths. Statistical comparisons showed that ensemble-refined annotations reduced noise and decreased mean offsets between annotations and detected objects, yielding more stable evaluation metrics. Our results demonstrate that ensemble-based ground truth generation can guide targeted revision of manual markers, provide a quality measure for annotation reliability, and generate new annotations where no human-generated landmarks exist. This methodology offers a generalizable strategy to strengthen annotation datasets in image cytometry, enabling robust algorithm evaluation in DNA ploidy analysis and potentially in other low-frequency assays. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare and Precision Medicine, 2nd Edition)

► Show Figures

Figure 1

15 pages, 3326 KB

Open AccessArticle

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 1267

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. 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)

► Show Figures

Figure 1

14 pages, 1520 KB

Open AccessArticle

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 1373

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 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)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI