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Electronics
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Artificial Intelligence and Big Data Processing in Healthcare
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Artificial Intelligence and Big Data Processing in Healthcare

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 May 2026 | Viewed by 3683

Special Issue Editors

Dr. Mohammad Masum

E-Mail Website
Guest Editor
Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA
Interests: machine learning; deep learning; tiny machine learning; health informatics; natural language processing; large language models; cyber security
Dr. Mohammed Chowdhury

E-Mail Website
Guest Editor
Department of Economics and Decision Science, Western Ilinois University, Macomb, IL 61455, USA
Interests: machine learning; biostatistics; health informatics
Dr. Sai Chandra Kosaraju

E-Mail Website
Guest Editor
Department of Computer Science, California State Polytechnic University, Pomana, CA 91768, USA
Interests: interpretable deep learning; machine learning; health informatics; bio-informatics
Dr. Saptarshi Sengupta

E-Mail Website
Guest Editor
Department of Computer Science, San Jose State University, San Jose, CA 95112, USA
Interests: cyber–physical systems; risk/reliability analysis; healthcare AI; complex systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of Artificial Intelligence (AI) and Big Data is transforming healthcare, offering unprecedented opportunities to enhance patient care, improve diagnostic accuracy, and optimize healthcare systems. As these technologies rapidly advance, their potential to transform healthcare has never been greater.

This Special Issue on "Artificial Intelligence and Big Data Processing in Healthcare" invites researchers, practitioners, and experts to share their latest findings and innovations that address the critical challenges in healthcare today. We seek high-quality, novel research demonstrating the latest advancements and practical applications, pushing the boundaries of what is possible with these transformative technologies.

The topics for this Special Issue cover a broad spectrum of cutting-edge applications. We explore how Large Language Models (LLMs) enhance clinical decision making, the emerging role of Generative AI in healthcare, and the integration of Tiny Machine Learning (TinyML) on edge devices. This issue also highlights human-centered AI that carefully considers ethical implications and the contributions of Machine Learning and Deep Learning in advancing precision medicine, medical image analysis, and using natural language processing to extract critical insights from clinical data. The transformative impact of Big Data Analytics on healthcare delivery is another key focus.

This Special Issue aims to supplement the existing literature with fresh insights and innovative solutions by addressing these diverse yet interconnected topics. We invite you to contribute your research and join this important dialogue, helping shape healthcare's future through innovation and collaboration. Topics of interest for submission include, but are not limited to, the following:

  1. Applications of Large Language Models (LLMs) in Healthcare;
  2. Generative AI in Healthcare;
  3. AI-Driven Smart Health Monitoring and Diagnostic Systems;
  4. Edge and Tiny Machine Learning (TinyML) for Next-Generation Healthcare;
  5. Human-Centred AI in Healthcare: Balancing Innovation and Ethics;
  6. Machine Learning and Deep Learning in Healthcare;
  7. Deep Learning for Precision Medicine;
  8. AI in Medical Image Analysis;
  9. Natural Language Processing in Healthcare;
  10. Big Data Analytics in Healthcare;

Dr. Mohammad Masum
Dr. Mohammed Chowdhury
Dr. Sai Chandra Kosaraju
Dr. Saptarshi Sengupta
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. Electronics 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
  • machine learning
  • precision medicine
  • natural language processing
  • deep learning
  • tiny machine learning (TinyML)

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Published Papers (3 papers)

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26 pages, 700 KB  
Article
Exploring AI in Healthcare Systems: A Study of Medical Applications and a Proposal for a Smart Clinical Assistant
by Răzvan Daniel Zota, Ionuț Alexandru Cîmpeanu and Mihai Adrian Lungu
Electronics 2025, 14(18), 3727; https://doi.org/10.3390/electronics14183727 - 20 Sep 2025
Viewed by 855
Abstract
The rising complexity and operational demands of modern healthcare systems have significantly increased resource usage and associated costs. This trend highlights the need for innovative approaches to optimize workflows and enhance decision-making. From this perspective, the present study explores how artificial intelligence (AI) [...] Read more.
The rising complexity and operational demands of modern healthcare systems have significantly increased resource usage and associated costs. This trend highlights the need for innovative approaches to optimize workflows and enhance decision-making. From this perspective, the present study explores how artificial intelligence (AI) can contribute to improving efficiency and information access in the medical field. The article begins with an introduction and a concise literature review focused on the integration of AI in healthcare platforms. Also, three main research questions are presented here. Our research employs an evaluation and a comparison for five existing medical-based applications. Each of these platforms was assessed to determine whether and how AI technologies have been integrated into their functionalities. The findings from this analysis inspired us to the design of a novel AI-based architecture, which we propose in section three of the article. This proposed architecture aims to assist medical professionals by providing streamlined access to relevant patient information, using machine learning (ML) techniques. Also, at the end of this section we address the initial research questions. In the final section of the article, we conclude that the insights gained from analyzing existing medical chatbot platforms has informed the design of our AI-based solution, aimed at supporting both patients and healthcare professionals through an integrated and intelligent system. The findings highlight the necessity for systems that not only align with user expectations but also demonstrate seamless integration within clinical workflows. Future research should prioritize advancing the reliability, personalization, and regulatory compliance of these platforms, thereby fostering enhanced patient engagement and enabling healthcare professionals to deliver care that is both more efficient and more accessible. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Processing in Healthcare)
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31 pages, 3576 KB  
Article
UltraScanNet: A Mamba-Inspired Hybrid Backbone for Breast Ultrasound Classification
by Alexandra-Gabriela Laicu-Hausberger and Călin-Adrian Popa
Electronics 2025, 14(18), 3633; https://doi.org/10.3390/electronics14183633 - 13 Sep 2025
Viewed by 436
Abstract
Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a specific deep learning backbone that addresses breast ultrasound classification [...] Read more.
Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a specific deep learning backbone that addresses breast ultrasound classification needs. The proposed architecture combines a convolutional stem with learnable 2D positional embeddings, followed by a hybrid stage that unites MobileViT blocks with spatial gating and convolutional residuals and two progressively global stages that use a depth-aware composition of three components: (1) UltraScanUnit (a state-space module with selective scan gated convolutional residuals and low-rank projections), (2) ConvAttnMixers for spatial channel mixing, and (3) multi-head self-attention blocks for global reasoning. This research includes a detailed ablation study to evaluate the individual impact of each architectural component. The results demonstrate that UltraScanNet reaches 91.67% top-1 accuracy, a precision score of 0.9072, a recall score of 0.9174, and an F1-score of 0.9096 on the BUSI dataset, which make it a very competitive option among multiple state-of-the-art models, including ViT-Small (91.67%), MaxViT-Tiny (91.67%), MambaVision (91.02%), Swin-Tiny (90.38%), ConvNeXt-Tiny (89.74%), and ResNet-50 (85.90%). On top of this, the paper provides an extensive global and per-class analysis of the performance of these models, offering a comprehensive benchmark for future work. The code will be publicly available. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Processing in Healthcare)
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17 pages, 2395 KB  
Article
Deep Learning for Non-Invasive Blood Pressure Monitoring: Model Performance and Quantization Trade-Offs
by Anbu Valluvan Devadasan, Saptarshi Sengupta and Mohammad Masum
Electronics 2025, 14(7), 1300; https://doi.org/10.3390/electronics14071300 - 26 Mar 2025
Cited by 1 | Viewed by 1825
Abstract
The development of non-invasive blood pressure monitoring systems remains a critical challenge, particularly in resource-constrained settings. This study proposes an efficient deep learning framework integrating Edge Artificial Intelligence for continuous blood pressure estimation using photoplethysmography (PPG) signals. We evaluate three architectures: a residual-enhanced [...] Read more.
The development of non-invasive blood pressure monitoring systems remains a critical challenge, particularly in resource-constrained settings. This study proposes an efficient deep learning framework integrating Edge Artificial Intelligence for continuous blood pressure estimation using photoplethysmography (PPG) signals. We evaluate three architectures: a residual-enhanced convolutional neural network, a transformer-based model, and an attentive BPNet. Using the MIMIC-IV waveform database, we implement a signal processing pipeline with adaptive filtering, statistical normalization, and peak-to-peak alignment. Experiments assess varying temporal windows (10 s, 20 s, 30 s) to optimize predictive accuracy and computational efficiency. Attentive BPNet achieves the best performance, with systolic blood pressure (SBP) estimation yielding a mean absolute error (MAE) of 6.36 mmHg, diastolic blood pressure (DBP) an MAE of 4.09 mmHg, and mean arterial pressure (MBP) an MAE of 4.56 mmHg. Post-training quantization reduces the model size by 90.71% (to 0.13 MB), enabling deployment on Edge devices. These findings demonstrate the feasibility of deploying deep learning-based continuous blood pressure monitoring on edge devices. The proposed framework provides a scalable and computationally efficient solution, offering real-time, accessible monitoring that could enhance hypertension management and optimize healthcare resource utilization. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Processing in Healthcare)
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