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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 November 2025 | Viewed by 1082

Special Issue Editors


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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

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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 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

  • 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 (1 paper)

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Research

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 503
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
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