Citation

Shaikh, Mohammad Shahnawaz and Kumar, Mukesh and Hussain, Naziya and Kondelwar, Anuradha and Pandwal, Akruti and Khan, Inam Ullah (2026) Explainable AI for Trustworthy Smart Healthcare Systems. In: Intelligent Techniques for Security of Medical Data. CRC Press, pp. 240-281. ISBN 978-100371540-5, 978-104114623-0, 978-104120170-0

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Abstract

It is already possible that Explainable Artificial Intelligence (XAI) emerges as a cornerstone to smart healthcare systems that are trustworthy. Considering that, modern hospitals are increasingly becoming dependent on AI-based diagnostics, Internet of Medical Things (IoMT) devices, wearables, and medical imaging, along with big-data analytics of Electronic Health Record (EHR) and saliency maps, and how they can contribute to improving the transparency, safety, and accountability of clinical results. Besides, a new integrated system comprising XAI, blockchain, and federated learning is suggested to resolve the issues of data privacy, auditability, and decentralized training. The framework will make sure that patient information is secure and allow credible AI-based insights in distributed healthcare organizations. The simulation study proves that this hybrid model is much more effective in improving trust, transparency, and security than traditional AI. The future research directions, which are also covered in the chapter, are hybrid XAI, cognitive digital twins, human-in-the-loop systems and quantum-enhanced explainability. In general, XAI turns out to be a primary facilitator of ethical, secure, and patient-centric smart healthcare ecosystems.

Item Type:	Book Section
Uncontrolled Keywords:	Big data, Cognitive systems, Data privacy
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Oct 2026 03:39
Last Modified:	28 Oct 2026 03:39
URII:	http://shdl.mmu.edu.my/id/eprint/16928

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