Explainable Machine Learning (XML) for Multimedia-Based Healthcare Systems: Opportunities, Challenges, Ethical and Future Prospects

Awotunde, Joseph Bamidele; Imoize, Agbotiname Lucky; Adeniyi, Abidemi Emmanuel; Abiodun, Kazeem Moses; Ayo, Emmanuel Femi; Kavitha, K. V. N.; Ajamu, Gbemisola Janet; Ogundokun, Roseline Oluwaseun

doi:10.1007/978-3-031-38036-5_2

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Book2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

AI-Driven Physical Rehabilitation Strategies in Post-Cancer Care

Al-Akayleh,

Al-Remawi,

Ali Agha

2024

2024 2nd International Conference on Cyber Resilience (ICCR)

View full text Add to dashboard Cite

AI-Driven Physical Rehabilitation Strategies in Post-Cancer Care

Al-Akayleh,

Al-Remawi,

Ali Agha

2024

2024 2nd International Conference on Cyber Resilience (ICCR)

View full text Add to dashboard Cite

Secure and Privacy-Preserving Federated Learning With Explainable Artificial Intelligence for Smart Healthcare Systems

Komalasari

2024

Advances in Healthcare Information Systems and Administration

View full text Add to dashboard Cite

With the escalating global population, the healthcare sector faces unprecedented challenges, necessitating innovative solutions. Deep learning (DL) and federated learning (FL) have emerged as pivotal technologies, yet challenges persist in data privacy, security, and model interpretability, especially in healthcare applications. This research addresses these challenges by proposing robust frameworks for secure, privacy-preserving federated learning with explainable artificial intelligence in smart healthcare systems. The objective is to enhance the security, performance, and privacy of healthcare systems, ensuring their resilience and effectiveness in real-world scenarios. The research employs a literature approach. This comprehensive approach establishes a foundation for the future development of smart healthcare systems, fostering trust, transparency, and efficiency in healthcare decision-making processes.

show abstract

Application of Blockchain-Based Internet of Things in Medical Healthcare

Adeniyi,

Awotunde,

Falola

et al. 2024

Advances in Civil and Industrial Engineering

View full text Add to dashboard Cite

Blockchain and the Internet of Things (IoT) are becoming pivotal in the IT industry, finding applications in sectors like supply chain, logistics, and the automotive industry. IoT devices often have limited processing and storage capabilities, leading to the centralization of user medical data in third-party repositories or cloud environments. Blockchain offers decentralized processing and storage for IoT data, making it an attractive option for creating decentralized IoT-enabled e-healthcare systems. This chapter begins with an overview of blockchain technology, followed by a discussion of prominent consensus algorithms within the context of e-health. It also evaluates various blockchain platforms for their suitability in IoT-based e-healthcare systems. Several case studies are presented methodically to demonstrate the utilization of IoT and blockchain core features in enhancing healthcare services and ecosystems.

show abstract

Explainable Machine Learning (XML) for Multimedia-Based Healthcare Systems: Opportunities, Challenges, Ethical and Future Prospects

Cited by 3 publications

References 98 publications

AI-Driven Physical Rehabilitation Strategies in Post-Cancer Care

AI-Driven Physical Rehabilitation Strategies in Post-Cancer Care

Secure and Privacy-Preserving Federated Learning With Explainable Artificial Intelligence for Smart Healthcare Systems

Application of Blockchain-Based Internet of Things in Medical Healthcare

Contact Info

Product

Resources

About