Submission Deadline: 31 May 2025 View: 561 Submit to Special Issue
Prof. Mohammad Mehedi Hassan, King Saud University, Saudi Arabia
Dr. Senthil Kumar Jagatheesaperumal, Mepco Schlenk Engineering College, India
Dr. Md Rafiul Hassan, Central Connecticut State University, USA
Artificial Intelligence (AI) has emerged as a significant driver of transformation in the healthcare sector, particularly in the realms of medical data management, integration, and analysis. With advancements in technology, medical data has become more accessible and adaptable to sophisticated processing techniques, offering immense potential for enhancing diagnostic accuracy, tailoring treatments, and improving overall patient care. However, this paradigm shift presents complex challenges, especially concerning data management, amidst evolving healthcare paradigms like patient-centric care, self-care initiatives, and integrated service delivery models.
This Special Issue endeavors to explore the multifaceted role of AI in healthcare, focusing on pivotal themes such as data management, integration strategies, data sharing protocols, patient privacy safeguards, and bioethical considerations. Despite the transformative promise of AI in revolutionizing clinical practice and decision-making processes, concerns persist regarding inherent biases in AI algorithms and the opacity of AI-driven decisions. Additionally, the escalating reliance on AI technology prompts inquiries into trust, privacy protection, and the ethical ramifications of data utilization in healthcare contexts.
Contributions solicited for this Special Issue are encouraged to delve into the following key areas:
· Interoperability and Standards in AI-driven Healthcare
· AI Algorithms for Healthcare Enhancement
· Explainability and Interpretability of AI Models
· Bias Detection and Mitigation Strategies for AI in Healthcare
· Ensuring Fairness and Equity in AI-Powered Treatment Recommendations
· Privacy Preservation in AI-Driven Healthcare Diagnosis
· Data Management and Integration in AI-driven Healthcare Systems
· Leveraging Non-Traditional Health Devices for Data Acquisition
· Human-AI Collaboration in Clinical Settings
· Identifying Barriers to AI Adoption in Healthcare
· Regulatory, Legal, and Ethical Considerations in AI-driven Healthcare
· Exploring Distributed Learning and Federated Data Systems in Healthcare
· Socioeconomic Impacts of AI in Healthcare
· Education and Training in AI for Healthcare Professionals
· AI-based Personalized Medicine and Precision Health
· Real-Time Health Monitoring and Predictive Analytics
· Natural Language Processing (NLP) for Clinical Documentation
· AI in Drug Discovery and Development
· Blockchain Technology in AI frameworks for Healthcare Data Security
· Metaverse-enabled Virtual Health Assistants
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