Guest Editors
Dr. Mohammad Shorfuzzaman
Email: m.shorf@tu.edu.sa
Affiliation: Department of Computer Science, College of Computers and IT, Taif University, Taif, Saudi Arabia
Homepage:
Research Interests: applied AI, smart health, medical image analysis, deep learning, computer vision, big data in healthcare, Internet of Things (IoT), and cloud computing
Dr. Mohammed Kaosar
Email: Mohammed.Kaosar@murdoch.edu.au
Affiliation: College of Arts, Business, Law and Social Sciences, Murdoch University, Perth, Australia
Homepage:
Dr. Lutful Karim
Email: lutful.karim@senecapolytechnic.ca
Affiliation: School of Information Technology Administration and Security, Seneca Polytechnic, Toronto, Ontario, Canada
Homepage:
Research Interests: computer and communication networks, wireless and mobile sensor networks, mobile and wireless computing, fault-tolerant computing systems, Internet of things (IoT) in healthcare, M2M communication, VANET, and big data analytics
Summary
Machine Learning (ML) and Deep Learning (DL) have revolutionized the landscape of healthcare by providing powerful tools for automating and enhancing the accuracy of medical diagnosis and treatment. From image analysis in radiology and pathology to predictive analytics for patient outcomes, these technologies have demonstrated remarkable potential to improve clinical decision-making. By analyzing vast amounts of medical data, ML and DL models can uncover complex patterns that assist in early disease detection, personalized treatment planning, and real-time monitoring of patient health.
However, while the promise of ML and DL in healthcare is immense, significant challenges remain in terms of scalability, interpretability, data privacy and security, and the integration of these systems into clinical workflows. This special issue aims to showcase the latest advancements in the application of ML and DL in medical diagnosis and treatment, addressing key challenges while highlighting innovative solutions and successful real-world implementations. Given the ongoing developments in AI technologies and the growing demand for precision medicine, this focus is both timely and essential.
We invite high-quality, original research papers, review articles, and case studies that explore the recent advancements in the application of ML and DL in medical diagnosis and treatment. Potential topics include, but are not limited to:
· Machine Learning for early disease detection
· Advances in DL techniques for analyzing medical images
· DL based medical image classification and segmentation
· Use of ML and DL for personalized treatment plans based on patient-specific data
· Predictive models for patient outcomes, disease progression, and treatment efficacy
· Applications of NLP techniques for extracting useful insights from electronic health records (EHRs) and clinical notes
· Automating the diagnosis and treatment recommendation process using NLP-driven systems
· Challenges and solutions for integrating ML/DL-based systems into existing clinical workflows
· Explainability and interpretability of AI models in medicine
· ML and DL approaches for accelerating drug discovery and designing personalized treatments
· Ethical use of ML and DL in healthcare, including patient privacy, bias in models, and fairness in AI-driven decisions
· Role of AI for remote healthcare and telemedicine
· Data privacy and security concerns for AI models in healthcare and telemedicine
Keywords
machine learning in healthcare, deep learning for medical diagnosis, medical image analysis, predictive analytics in medicine, AI in personalized treatment, ethical AI in healthcare
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