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Applied Sciences
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Big Data Integration and Artificial Intelligence in Medical Systems
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Big Data Integration and Artificial Intelligence in Medical Systems

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

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 961

Special Issue Editors

Dr. Liaoyuan Zeng

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China
Interests: intelligent processing of signals and information; health informatics; big data analytics in medicine; clinical decision support systems; artificial intelligence in healthcare
Dr. Yiming Lei

E-Mail Website
Guest Editor
School of Electronics, Peking University, Beijing 100871, China
Interests: electronic and communication engineering; biophysics and bioelectronics; artificial intelligence and big data analytics in medicine; medical equipment

Special Issue Information

Dear Colleagues,

In recent years, the convergence of big data technologies and artificial intelligence (AI) has ushered in a new era of medical systems—comprehensive, data-driven frameworks encompassing clinical diagnostics, decision support, treatment management, patient engagement, and healthcare service delivery. This Special Issue aims to highlight the most recent advances in medical system intelligence through the integration of heterogeneous medical data and state-of-the-art computational methods.

We particularly encourage submissions that address the challenges in multi-modal data fusion, machine learning models for disease detection and decision support, real-time monitoring solutions, and interpretable AI in clinical settings. Articles that present applications of deep learning, federated learning, generative models, and privacy-aware computation to medical tasks—especially those with validated impact on the healthcare delivery—are highly welcomed.

This Special Issue seeks to foster a multidisciplinary perspective that bridges data science and medicine by showcasing effective methodologies, robust frameworks, and scalable implementations. We also encourage the use and creation of open-access datasets and reproducible benchmarks that advance collaborative research in AI-enabled medical systems. The ultimate objective is to deepen our understanding of current capabilities and future opportunities in building intelligent, interoperable, and trustworthy healthcare solutions.

Dr. Liaoyuan Zeng
Dr. Yiming Lei
Guest Editors

Manuscript Submission Information

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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 in healthcare
  • big data analytics in medicine
  • clinical decision support systems (CDSS)
  • multimodal data fusion
  • deep learning in medical imaging
  • federated learning in healthcare
  • explainable AI (XAI)
  • predictive modeling in healthcare
  • health informatics
  • personalized and precision medicine
  • synthetic data generation in healthcare
  • privacy-preserving machine learning
  • wearable health technology
  • natural language processing in clinical data
  • cloud computing in health data management
  • real-time health monitoring systems
  • generative AI in medical applications
  • medical Internet of Things (IoT)
  • electronic health record (EHR) integration
  • AI-driven drug discovery

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Published Papers (1 paper)

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Research

23 pages, 3135 KB  
Article
Clinically Oriented Evaluation of Transfer Learning Strategies for Cross-Site Breast Cancer Histopathology Classification
by Liana Stanescu and Cosmin Stoica-Spahiu
Appl. Sci. 2025, 15(23), 12819; https://doi.org/10.3390/app152312819 - 4 Dec 2025
Viewed by 410
Abstract
Background/Objectives: Breast cancer diagnosis based on histopathological examination remains the most reliable and widely accepted approach in clinical practice, despite being time-consuming and prone to inter-observer variability. While deep learning methods have achieved high accuracy in medical image classification, their cross-site generalization [...] Read more.
Background/Objectives: Breast cancer diagnosis based on histopathological examination remains the most reliable and widely accepted approach in clinical practice, despite being time-consuming and prone to inter-observer variability. While deep learning methods have achieved high accuracy in medical image classification, their cross-site generalization remains limited due to differences in staining protocols and image acquisition. This study aims to evaluate and compare three clinically relevant adaptation strategies to improve model robustness under domain shift. Methods: The ResNet50V2 model, pretrained on ImageNet and further fine-tuned on the Kaggle Breast Histopathology Images dataset, was subsequently adapted to the BreaKHis dataset under three clinically relevant transfer strategies: (i) threshold calibration without retraining (site calibration), (ii) head-only fine-tuning (light FT), and (iii) full fine-tuning (full FT). Experiments were performed on an internal balanced dataset and on the public BreaKHis dataset using strict patient-level splitting to avoid data leakage. Evaluation metrics included accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC, computed per magnification level (40×, 100×, 200×, 400×). Results: Full fine-tuning consistently yielded the highest performance across all magnifications, reaching up to 0.983 ROC-AUC and 0.980 sensitivity at 400×. At 40× and 100×, the model correctly identified over 90% of malignant cases, with ROC-AUC values of 0.9500 and 0.9332, respectively. Head-only fine-tuning led to moderate gains (e.g., sensitivity up to 0.859 at 200×), while threshold calibration showed limited improvements (ROC-AUC ranging between 0.60–0.73). Grad-CAM analysis revealed more stable and focused attention maps after full fine-tuning, though they did not always align with diagnostically relevant regions. Conclusions: Our findings confirm that full fine-tuning is essential for robust cross-site deployment of histopathology AI systems, particularly at high magnifications. Lighter strategies such as threshold calibration or head-only fine-tuning may serve as practical alternatives in resource-constrained environments where retraining is not feasible. Full article
(This article belongs to the Special Issue Big Data Integration and Artificial Intelligence in Medical Systems)
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