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Applied Sciences
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Advances in Digital Health: IoT, AI-Driven Diagnosis, and Data Security
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Advances in Digital Health: IoT, AI-Driven Diagnosis, and Data Security

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 September 2025 | Viewed by 888

Special Issue Editor

Dr. Nico Surantha

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Communication Engineering, Tokyo City University, Tokyo 158-8557, Japan
Interests: communication and network engineering; electron device and electronic equipment; manufacturing technology; information technology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Innovations in digital health could transform the delivery of healthcare worldwide by enabling remote monitoring, individualized care, and intelligent diagnostics. The growing deployment of Internet of Things (IoT) devices, combined with machine learning and artificial intelligence (AI), has accelerated the development of remote health monitoring platforms and cutting-edge diagnostic tools. However, with the proliferation of sensitive patient data across various systems, challenges associated with privacy and security has arisen, and regulatory frameworks must evolve to safeguard patients and providers alike.

This Special Issue welcomes the submission of novel research, reviews, and case studies that explore the latest advancements in IoT-based healthcare solutions, AI-driven medical diagnostics, secure data sharing, and emerging frameworks that protect patient data. The scope of this Special Issue includes, but is not limited to, the following topics:

  • New IoT architectures and devices for real-time health monitoring and personalized medicine.
  • Machine learning and AI methodologies that enhance diagnostic accuracy and clinical decision-making.
  • Data privacy, security protocols, and governance strategies for safeguarding sensitive health information.
  • The ethical and regulatory considerations associated with digital health platforms, including best practices for ensuring patient autonomy and informed consent.
  • Interdisciplinary frameworks that integrate sensors, telemedicine, and mobile applications to optimize patient engagement and outcomes.

By connecting researchers, clinicians, and industry experts, this Special Issue aims to provide insights into the design, implementation, and regulation of secure, patient-centered digital health solutions. We welcome the submission of experimental findings, conceptual designs, implementation frameworks, and practical evaluations that could shape the next generation of connected healthcare.

Dr. Nico Surantha
Guest Editor

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

  • digital health
  • IoT
  • AI-driven medical diagnosis
  • health monitoring
  • health data security
  • data privacy
  • edge computing

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Published Papers (1 paper)

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Research

15 pages, 4629 KiB  
Article
Performance Evaluation of Convolutional Neural Network (CNN) for Skin Cancer Detection on Edge Computing Devices
by Vincent, Garry Darian and Nico Surantha
Appl. Sci. 2025, 15(6), 3077; https://doi.org/10.3390/app15063077 - 12 Mar 2025
Viewed by 803
Abstract
Skin cancer is one of the most common and life-threatening diseases. In the current era, early detection remains a significant challenge, particularly in remote and underserved regions with limited internet access. Traditional skin cancer detection systems often depend on image classification using deep [...] Read more.
Skin cancer is one of the most common and life-threatening diseases. In the current era, early detection remains a significant challenge, particularly in remote and underserved regions with limited internet access. Traditional skin cancer detection systems often depend on image classification using deep learning models that require constant connectivity to internet access, creating barriers in areas with poor infrastructure. To address this limitation, CNN provides an innovative solution by enabling on-device machine learning on low-computing Internet of Things (IoT) devices. This study evaluates the performance of a convolutional neural network (CNN) model trained on 10,000 dermoscopic images spanning seven classes from the Harvard Skin Lesion dataset. Unlike previous research, which seldom offers detailed performance evaluations on IoT hardware, this work benchmarks the CNN model on multiple single-board computers (SBCs), including low-computing devices like Raspberry Pi and Jetson Nano. The evaluation focuses on classification accuracy and hardware efficiency, analyzing the impact of varying training dataset sizes to assess the model’s scalability and effectiveness on resource-constrained devices. The simulation results demonstrate the feasibility of deploying accurate and efficient skin cancer detection systems directly on low-power hardware. The simulation results show that our proposed method achieves an accuracy of 98.25%, with the fastest hardware being the Raspberry Pi 5, which achieves a detection time of 0.01 s. Full article
(This article belongs to the Special Issue Advances in Digital Health: IoT, AI-Driven Diagnosis, and Data Security)
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