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Applied Sciences
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Generative Artificial Intelligence for Clinical Decision Support System and Healthcare
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Generative Artificial Intelligence for Clinical Decision Support System and Healthcare

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

Deadline for manuscript submissions: 20 June 2026 | Viewed by 4777

Special Issue Editors

Dr. Giacomo Cappon

E-Mail Website
Guest Editor
Department of Information Engineering (DEI), University of Padova, 35131 Padova, Italy
Interests: artificial intelligence; Bayesian methods; diabetes; signal processing; decision support systems; digital health and therapeutics; telemedicine
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Cossu

E-Mail Website
Guest Editor Assistant
Department of Information Engineering (DEI), University of Padova, 35131 Padova, Italy
Interests: artificial intelligence; wearable devices; clinical usability; digital health; decision support systems; diabetes; clinical data visualization and use

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue titled “Generative Artificial Intelligence for Clinical Decision Support System and Healthcare”. As healthcare continues to evolve with the integration of cutting-edge digital technologies, Generative Artificial Intelligence (GenAI) is emerging as a key enabler in transforming clinical workflows, enhancing decision-making, and personalizing patient care.

This Special Issue aims to highlight innovative methodologies, formative use cases, and impactful applications of GenAI in clinical decision support systems (CDSS) and across the healthcare continuum. We welcome original research and review contributions that explore how GenAI—through models such as large language models, generative adversarial networks, and diffusion models—is being integrated into healthcare environments to improve efficiency, accuracy, and outcomes.

By contributing to this Special Issue, you will have the opportunity to disseminate your work to a multidisciplinary audience of researchers, clinicians, and healthcare technology experts. This issue aims to foster meaningful dialog across disciplines, encourage the translation of generative AI innovations into clinical practice, and support the development of robust, ethical, and scalable solutions that address real-world healthcare challenges.

We invite contributions that explore the transformative role of GenAI in healthcare. All submissions will undergo a rigorous peer-review process to ensure scientific excellence.

Topics of interest include, but are not limited to:

  • GenAI-powered systems for clinical note generation and EHR summarization;
  • AI copilots in diagnostic workflows and decision support;
  • Virtual assistants for patient communication, triage, or education;
  • Integration of GenAI into multidisciplinary clinical settings;
  • Use of GenAI for precision medicine;
  • Educational and training tools enhanced by GenAI technologies;
  • Ethical, regulatory, and transparency considerations in GenAI applications;
  • Explainable generative models for clinical interpretability and trust.

Dr. Giacomo Cappon
Guest Editor

Dr. Luca Cossu
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 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

  • generative AI
  • GPT in healthcare
  • decision support system
  • clinical decision-making
  • large language models
  • explainable AI in medicine
  • AI in clinical workflows
  • human-AI collaboration in medicine

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Published Papers (3 papers)

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Research

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18 pages, 2231 KB  
Article
An Open, Harmonized Genomic Meta-Database Enabling AI-Based Personalization of Adjuvant Chemotherapy in Early-Stage Non-Small Cell Lung Cancer
by Hojin Moon, Michelle Y. Cheuk, Owen Sun, Katherine Lee, Gyumin Kim, Kaden Kwak, Koeun Kwak and Aaron C. Tam
Appl. Sci. 2025, 15(19), 10733; https://doi.org/10.3390/app151910733 - 5 Oct 2025
Viewed by 1360
Abstract
Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well [...] Read more.
Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence for Clinical Decision Support System and Healthcare)
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Review

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26 pages, 793 KB  
Review
Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration
by Corrado Zengarini, Nico Curti, Stephano Cedirian, Luca Rapparini, Francesca Pampaloni, Alessandro Pileri, Francesco Durazzi, Martina Mussi, Michelangelo La Placa, Bianca Maria Piraccini and Michela Starace
Appl. Sci. 2026, 16(7), 3199; https://doi.org/10.3390/app16073199 - 26 Mar 2026
Viewed by 319
Abstract
This scoping review summarizes current computational image analysis and artificial intelligence (AI) approaches for the assessment of hair and scalp disorders, with emphasis on quantitative trichoscopy and operator-independent evaluation. A deep Medline search was performed using a citation network-based approach using MeSH terms [...] Read more.
This scoping review summarizes current computational image analysis and artificial intelligence (AI) approaches for the assessment of hair and scalp disorders, with emphasis on quantitative trichoscopy and operator-independent evaluation. A deep Medline search was performed using a citation network-based approach using MeSH terms and complementary keywords covering diagnostic imaging, trichoscopy/videodermoscopy, image processing, algorithms, AI, and mobile/smartphone-based workflows. Overall, relatively few studies assess algorithms in real-world clinical pathways, and much of the retrieved literature is predominantly pre-clinical or methodology-driven. In parallel, commercially available AI-assisted trichoscopy platforms have little or no traceable peer-reviewed evidence; their validation methods and underlying datasets are often proprietary, undisclosed, and not directly comparable, limiting independent verification and cross-platform benchmarking. The most mature academic applications focus on follicular unit quantification (hair density, shaft diameter distribution, vellus-to-terminal ratio, and severity mapping), mainly using convolutional neural networks with object detection and instance segmentation. In conclusion, AI-assisted trichoscopy may support a shift toward standardized quantitative outputs, but clinical translation remains early and constrained by small or proprietary datasets, heterogeneous acquisition/annotation protocols, limited external validation, and scarce prospective studies. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence for Clinical Decision Support System and Healthcare)
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22 pages, 670 KB  
Review
Transition to Artificial Intelligence in Imaging and Laboratory Diagnostics in Rheumatology
by Stoimen Dimitrov, Simona Bogdanova, Zhaklin Apostolova, Boryana Kasapska, Plamena Kabakchieva and Tsvetoslav Georgiev
Appl. Sci. 2025, 15(21), 11666; https://doi.org/10.3390/app152111666 - 31 Oct 2025
Viewed by 2107
Abstract
Artificial intelligence (AI) is rapidly transforming rheumatology, particularly in imaging and laboratory diagnostics where data complexity challenges traditional interpretation. This narrative review summarizes current evidence on AI-driven tools across musculoskeletal ultrasound, radiography, MRI, CT, capillaroscopy, and laboratory analytics. A structured literature search (PubMed, [...] Read more.
Artificial intelligence (AI) is rapidly transforming rheumatology, particularly in imaging and laboratory diagnostics where data complexity challenges traditional interpretation. This narrative review summarizes current evidence on AI-driven tools across musculoskeletal ultrasound, radiography, MRI, CT, capillaroscopy, and laboratory analytics. A structured literature search (PubMed, Scopus, Web of Science; 2020–2025) identified 90 relevant publications addressing AI applications in diagnostic imaging and biomarker analysis in rheumatic diseases, while twelve supplementary articles were incorporated to provide contextual depth and support conceptual framing. Deep learning models, notably convolutional neural networks and vision transformers, have demonstrated expert-level accuracy in detecting synovitis, bone marrow edema, erosions, and interstitial lung disease, as well as in quantifying microvascular and structural damage. In laboratory diagnostics, AI enhances the integration of traditional biomarkers with high-throughput omics, automates serologic interpretation, and supports molecular and proteomic biomarker discovery. Multi-omics and explainable AI platforms increasingly enable precision diagnostics and personalized risk stratification. Despite promising performance, widespread implementation is constrained by significant domain-specific validation gaps, data heterogeneity, lack of external validation, ethical concerns, and limited workflow integration. Clinically meaningful progress will depend on transparent, validated, and interoperable AI systems supported by robust data governance and clinician education. The transition from concept to clinic is under way—AI will likely serve as an augmenting rather than replacing partner, standardizing interpretation, accelerating decision-making, and ultimately facilitating precision, data-driven rheumatologic care. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence for Clinical Decision Support System and Healthcare)
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