Artificial Intelligence for Healthcare

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 May 2024 | Viewed by 1871

Special Issue Editors

Department of Computer Science and Software Engineering, University of Salford, Salford M5 4WT, UK
Interests: digital health; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Multimedia Communication and Intelligent Control, School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
Interests: prediction and control of video quality using AI, ML, cloud computing, fuzzy logic, applying computer vision techniques, and deep learning in pedestrian recognition; disease identification in cotton crops and damage recognition in wind turbines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has significantly reshaped healthcare. AI algorithms have been helpful in analysing large datasets, which has resulted in successful outcomes such as making clinical decisions, interpreting medical images and predicting clinical outcomes. Despite the successes of AI in healthcare, there are challenges that exist in the field. These challenges include trust issues, bias in datasets, regulation and lack of evidence in clinical settings.

In this Special Issue, the aim is to publish high-quality articles and reviews that address the challenges of AI in healthcare in the following areas (but not limited to):

  • Machine learning;
  • Computer vision;
  • Deep learning;
  • Neural networks;
  • Natural language processing;
  • Robotics;
  • Computational and data science;
  • Fuzzy logic;
  • Remote monitoring using AI techniques;
  • Medical imaging;
  • Responsible AI;
  • Drug discovery using AI techniques;
  • AI implementations in clinical settings.

Dr. Gloria Iyawa
Dr. Asiya Khan
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. 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
  • artificial intelligence
  • machine learning
  • responsible AI

Published Papers (2 papers)

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18 pages, 3048 KiB  
Article
Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution
by Eugenia I. Toki, Giorgos Tatsis, Jenny Pange and Ioannis G. Tsoulos
Appl. Sci. 2024, 14(1), 305; https://doi.org/10.3390/app14010305 - 29 Dec 2023
Viewed by 440
Abstract
Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level [...] Read more.
Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one. Full article
(This article belongs to the Special Issue Artificial Intelligence for Healthcare)
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24 pages, 991 KiB  
Systematic Review
Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review
by Haifa Almutairi, Ghulam Mubashar Hassan and Amitava Datta
Appl. Sci. 2023, 13(24), 13280; https://doi.org/10.3390/app132413280 - 15 Dec 2023
Viewed by 1096
Abstract
Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological [...] Read more.
Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research. Full article
(This article belongs to the Special Issue Artificial Intelligence for Healthcare)
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