Artificial Intelligence and Machine Learning in Healthcare Applications

Submission Deadline: 28 February 2026 (closed) View: 1625 Submit to Journal

Guest Editor(s)

Prof. Neil Vaughan

Email: n.vaughan@exeter.ac.uk

Affiliation: Department of Clinical and Biomedical Sciences (CBS), University of Exeter, Exeter, CH2 5DW, United Kingdom

Homepage:

Research Interests: AI, virtual reality (VR), 3D medical computer graphics, smartphone apps and devices for healthcare

Summary

This Special Issue on Artificial Intelligence (AI) and Machine Learning (ML) in Healthcare Technologies explores the transformative role of intelligent systems in modern healthcare. As digital health solutions evolve, AI and ML have emerged as pivotal tools for enhancing diagnostic accuracy, treatment personalization, operational efficiency, and predictive analytics. The introduction emphasizes the growing importance of these technologies in addressing global healthcare challenges, including aging populations, chronic disease management, and limited clinical resources.

The aim of this Special Issue is to showcase cutting-edge research, innovative applications, and theoretical advancements at the intersection of AI, ML, and healthcare. It seeks to foster interdisciplinary collaboration and present novel insights that support the development of intelligent, patient-centered healthcare systems. The scope encompasses both methodological innovations and real-world implementations, covering diverse healthcare environments.

Suggested themes include, but are not limited to: AI-driven diagnostics and imaging, predictive modeling for patient outcomes, ML applications in personalised medicine, natural language processing (NLP) in clinical documentation, wearable and remote monitoring technologies, ethical and regulatory considerations in AI deployment, and the integration of AI into electronic health records (EHRs), Ethical, responsible and explainable AI in healthcare, Regulatory frameworks or clinical adoption of AI healthcare.

By collecting high-quality contributions, this Special Issue aims to advance the understanding and responsible adoption of AI and ML in improving healthcare delivery and outcomes.

Keywords

artificial intelligence (AI), machine learning, healthcare applications, virtual reality (VR), medical graphics, smartphone apps, health technology, wearable devices

Published Papers

Show export options

Open Access

ARTICLE

A Multi-Input Neural Network for Early Detection of Neurological Disordersfrom Vocal and Sleep Data
Md. Shujan Shak, Nabila Rahman, Fuad Mahmud, Ashim Chandra Das, M. F. Mridha, Md. Jakir Hossen
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081102
（This article belongs to the Special Issue: Artificial Intelligence and Machine Learning in Healthcare Applications)
Abstract Early detection of neurological disorders is critical for effective treatment planning and improved quality of life. This study proposes a multi-input deep neural network that integrates vocal biomarkers and clinical sleep-related features to improve diagnostic accuracy. The model processes each modality through separate neural branches before combining high-level representations for final classification. We evaluate the approach using two public datasets: a Parkinson’s disease dataset containing 1195 voice samples and a sleep-disorder dataset with 80 patient records. Experimental results show that the proposed model outperforms classical machine learning baselines and single-modality deep learning models, achieving an More >

View
39

Download
7
Open Access

ARTICLE

Effective Deep Learning Models for the Semantic Segmentation of 3D Human MRI Kidney Images
Roshni Khedgaonkar, Pravinkumar Sonsare, Kavita Singh, Ayman Altameem, Hameed R. Farhan, Salil Bharany, Ateeq Ur Rehman, Ahmad Almogren
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072651
（This article belongs to the Special Issue: Artificial Intelligence and Machine Learning in Healthcare Applications)
Abstract Recent studies indicate that millions of individuals suffer from renal diseases, with renal carcinoma, a type of kidney cancer, emerging as both a chronic illness and a significant cause of mortality. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have become essential tools for diagnosing and assessing kidney disorders. However, accurate analysis of these medical images is critical for detecting and evaluating tumor severity. This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images. The proposed framework fuses a customized U-Net and Mask R-CNN… More >

View
1998

Download
703

Artificial Intelligence and Machine Learning in Healthcare Applications

Guest Editor(s)

Summary

Keywords

Published Papers

View

39

Download

7

View

1998

Download

703

Further Information

Guidelines

Follow Us

Join Us

Contact Us

WhatsApp:

Share Link