Explainable and Trustworthy AI for Healthcare 4.0: A Systematic Review
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Abstract
The integration of Artificial Intelligence into healthcare systems has revolutionized diagnosis, treatment planning, and patient monitoring. However, the lack of transparency in AI models poses challenges in trust and adoption. This paper presents a systematic review of Explainable AI (XAI) techniques in Healthcare 4.0. It analyzes methods such as SHAP, LIME, and attention-based models for improving interpretability in clinical decision-making. The study evaluates applications in medical imaging, predictive diagnostics, and personalized medicine. Key challenges, including regulatory compliance, ethical concerns, and data bias, are discussed. The paper concludes with future research directions focusing on human-centered AI and robust validation frameworks.
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References
Whig, P., Sharma, P., Elngar, A. A., & Silva, N. (2025). Artificial intelligence and machine learning for advanced data-driven systems. CRC Press.
Whig, P., Sharma, P., & Yathiraju, N. (2025). Cutting-edge solutions for advancing sustainable development: Exploring technological horizons for sustainability (Part 1). Bentham Science Publishers.
Whig, P., Yathiraju, N., & Sharma, P. (2025). Cutting-edge solutions for advancing sustainable development: Exploring technological horizons for sustainability (Part 2). Bentham Science Publishers.
Whig, P., & Elngar, A. (2025). AI innovations for transforming food production. IGI Global.
Whig, P., & Elngar, A. (2025). Modernizing the food industry: AI-powered infrastructure, security, and supply chain innovation. IGI Global.
Whig, P., Sharma, P., Elngar, A. A., & Silva, N. (2025). Quantum learning: Bridging artificial intelligence, quantum computing, and data science in education. CRC Press.
Rather, R., Vats, H., Sharma, S., & Whig, P. (2025). AI and ML applications in data-informed leadership: Transforming higher education. In Advances in data science driven technologies (Vol. 5, pp. 36–53). Bentham Science.
Whig, P., Silva, N., Elngar, A. A., Aneja, N., & Sharma, P. (2025). Sustainable development through machine learning, AI and IoT: Proceedings of ICSD 2025 (Part I). Springer.
Whig, P., Silva, N., Elngar, A. A., Aneja, N., & Sharma, P. (2025). Sustainable development through machine learning, AI and IoT: Proceedings of ICSD 2025 (Part II). Springer.
Whig, P., et al. (2025). Shield-Pay: Secure healthcare encryption and integrated ledger for data and payments authentication yield (Patent application). Government of India.