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Healthcare
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Digital Transformation in Healthcare: Second Edition
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Digital Transformation in Healthcare: Second Edition

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 14082

Special Issue Editor

Dr. Marco P. Soares dos Santos

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal
Interests: bioelectronic implants; sensors for medical devices; biophysical stimulation of biological tissues; energy harvesting to power intracorporeal medical devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This issue aims to provide a revealing overview of the impact of advanced computation and instrumentation on healthcare. A worldwide increasing trend is driving innovation for a new era of multifunctional technologies with the ability to autonomously perform intensive therapeutic actuation, physiologic monitoring, digital processing, and communication operations. Products and services are being researched to increasingly incorporate computational capabilities. Research into healthcare is nowadays performed on a multidisciplinary basis, comprising computational engineering, biomedicine, biomedical engineering, electronic engineering, and automation engineering, among other areas. This Special Issue aims to disseminate cutting-edge research focused on ten topics: (1) personalized healthcare; (2) big data in healthcare; (3) predictive healthcare; (4) virtual reality in healthcare; (5) telemedicine; (6) artificial intelligence in healthcare; (7) bioelectronic medicine; (8) innovative medical devices—wearable medical devices and bioelectronic/instrumented implants; (9) modeling and simulation in healthcare; (10) energy harvesting to power medical devices. This interdisciplinary forum encourages the submission of original research, reviews, short reports, and opinion papers.

Dr. Marco P. Soares dos Santos
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. Healthcare 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 2700 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.

Published Papers (3 papers)

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Research

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11 pages, 255 KiB  
Article
Digital Health Training, Attitudes and Intentions to Use It among Romanian Medical Students: A Study Performed during COVID-19 Pandemic
by Lucia Maria Lotrean and Simina Antonia Sabo
Healthcare 2023, 11(12), 1731; https://doi.org/10.3390/healthcare11121731 - 13 Jun 2023
Cited by 1 | Viewed by 1177
Abstract
Introduction: This study focuses on medical students from the University of Medicine and Pharmacy in Cluj-Napoca, Romania, and has three objectives. First, it evaluates the opinions of medical students regarding their previous training as well as their needs for future training in the [...] Read more.
Introduction: This study focuses on medical students from the University of Medicine and Pharmacy in Cluj-Napoca, Romania, and has three objectives. First, it evaluates the opinions of medical students regarding their previous training as well as their needs for future training in the field of digital health. Second, it assesses their attitudes regarding digital health and their intention to use digital tools as physicians. Lastly, the interrelationship between these issues as well as the socio-demographic factors which influence them are investigated. Materials and methods: A cross-sectional survey was performed during June–August 2021 among fifth and sixth year students of the Faculty of Medicine from the Iuliu Hațieganu University of Medicine and Pharmacy in Cluj-Napoca, Romania. Anonymous online questionnaires were used which were filled in by 306 students. Results: Less than half of the participating students declared that they benefited from training or different practical examples during medical education regarding the use of digital tools in different medical areas, while the majority said that they would like to receive more training in the field of digital health. A total of 58.2% said that they totally agree with the introduction of a formal training in the medical curricula regarding digital health. Many students declared positive attitudes toward the use of digital tools in different domains within the medical field and intention to use digital tools as physicians; several differences were noted, including gender, year of study, type of domain, and previous training with regard to the use of digital tools in those domains. Moreover, the need for future training and the desire for the introduction of a formal training program into the medical curricula with regard to this field were stronger among those with more positive attitudes and higher intentions to use digital tools in their medical activity. Conclusions: To the best of our knowledge, this is the first study from Romania which investigated the training, attitudes, and intentions regarding the use of digital health among Romanian medical students, and offers valuable information to guide the education of medical students. Full article
(This article belongs to the Special Issue Digital Transformation in Healthcare: Second Edition)
22 pages, 9505 KiB  
Article
Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks
by Ahsan Shahzad, Abid Mushtaq, Abdul Quddoos Sabeeh, Yazeed Yasin Ghadi, Zohaib Mushtaq, Saad Arif, Muhammad Zia ur Rehman, Muhammad Farrukh Qureshi and Faisal Jamil
Healthcare 2023, 11(10), 1493; https://doi.org/10.3390/healthcare11101493 - 20 May 2023
Cited by 8 | Viewed by 4100
Abstract
Fibroids of the uterus are a common benign tumor affecting women of childbearing age. Uterine fibroids (UF) can be effectively treated with earlier identification and diagnosis. Its automated diagnosis from medical images is an area where deep learning (DL)-based algorithms have demonstrated promising [...] Read more.
Fibroids of the uterus are a common benign tumor affecting women of childbearing age. Uterine fibroids (UF) can be effectively treated with earlier identification and diagnosis. Its automated diagnosis from medical images is an area where deep learning (DL)-based algorithms have demonstrated promising results. In this research, we evaluated state-of-the-art DL architectures VGG16, ResNet50, InceptionV3, and our proposed innovative dual-path deep convolutional neural network (DPCNN) architecture for UF detection tasks. Using preprocessing methods including scaling, normalization, and data augmentation, an ultrasound image dataset from Kaggle is prepared for use. After the images are used to train and validate the DL models, the model performance is evaluated using different measures. When compared to existing DL models, our suggested DPCNN architecture achieved the highest accuracy of 99.8 percent. Findings show that pre-trained deep-learning model performance for UF diagnosis from medical images may significantly improve with the application of fine-tuning strategies. In particular, the InceptionV3 model achieved 90% accuracy, with the ResNet50 model achieving 89% accuracy. It should be noted that the VGG16 model was found to have a lower accuracy level of 85%. Our findings show that DL-based methods can be effectively utilized to facilitate automated UF detection from medical images. Further research in this area holds great potential and could lead to the creation of cutting-edge computer-aided diagnosis systems. To further advance the state-of-the-art in medical imaging analysis, the DL community is invited to investigate these lines of research. Although our proposed innovative DPCNN architecture performed best, fine-tuned versions of pre-trained models like InceptionV3 and ResNet50 also delivered strong results. This work lays the foundation for future studies and has the potential to enhance the precision and suitability with which UF is detected. Full article
(This article belongs to the Special Issue Digital Transformation in Healthcare: Second Edition)
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Review

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45 pages, 2469 KiB  
Review
Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey
by Sahalu Balarabe Junaid, Abdullahi Abubakar Imam, Abdullateef Oluwagbemiga Balogun, Liyanage Chandratilak De Silva, Yusuf Alhaji Surakat, Ganesh Kumar, Muhammad Abdulkarim, Aliyu Nuhu Shuaibu, Aliyu Garba, Yusra Sahalu, Abdullahi Mohammed, Tanko Yahaya Mohammed, Bashir Abubakar Abdulkadir, Abdallah Alkali Abba, Nana Aliyu Iliyasu Kakumi and Saipunidzam Mahamad
Healthcare 2022, 10(10), 1940; https://doi.org/10.3390/healthcare10101940 - 3 Oct 2022
Cited by 33 | Viewed by 8202
Abstract
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have [...] Read more.
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS. Full article
(This article belongs to the Special Issue Digital Transformation in Healthcare: Second Edition)
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