Open Access

REVIEW

Explainable, Context-Aware Artificial Intelligence and Digital Twin in Healthcare: A Systematic Review and Unified Taxonomy

Abdelzahir Abdelmaboud¹, Ibrahim Hameed², Salmah Fattah³, Ashraf Osman Ibrahim⁴, Salil Bharany⁵, Ateeq Ur Rehman^6,7,8, SeongKi Kim^9,10,*

1 Humanities Research Center, Sultan Qaboos University, Muscat, Oman
2 Faculy of Computer Studies, International University of Africa, Khartoum, Sudan
3 Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
4 Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
5 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
6 Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India
7 International Center for Materials Sciences and Technology, Western Caspian University, Baku, Azerbaijan
8 Centre for Promotion of Research, Graphic Era (Deemed to be University), Dehradun, India
9 Institute of Well-Aging Medicare & CSU G-LAMP Project Group, Chosun University, Gwangju, Republic of Korea
10 Department of Computer Engineering, Chosun University, Gwangju, Republic of Korea

* Corresponding Author: SeongKi Kim. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences 2026, 148(3), 5 https://doi.org/10.32604/cmes.2026.088032

Received 26 June 2026; Accepted 04 September 2026; Issue published 28 September 2026

Abstract

This paper provides a thorough, systematic review of explainable, context-sensitive Artificial Intelligence (AI) and Digital Twin (DT) technologies in healthcare, emphasizing their potential to transform the medical system into an intelligent, patient-centred system of care. The PRISMA framework helped identify 1842 records from the largest scientific databases, of which 97 high-quality studies were selected for a comprehensive analysis. The review analyses in detail the development of AI from traditional machine learning to modern deep learning and generative models with a focus on their use in disease diagnosis and predictive analytics, personalized medicine, and healthcare management. One of the priorities is the incorporation of Explainable AI (XAI), which tackles the significant issue of the black-box character of AI by making it more transparent, interpretable, and trusted by the clinical decision-making process. In addition, the paper examines how DT technology can be used to produce dynamic virtual models of patients and healthcare systems that can be monitored in real time, simulated, and analyzed to predict outcomes. An alternative, multi-layered taxonomy is proposed to bring together AI, XAI, DTs, and other emerging technologies, such as Internet of Things (IoT) and federated learning, within a unified, context-specific healthcare framework. The results show that although these technologies greatly enhance diagnostic accuracy, operational efficiency, and personalized care, difficulties such as data privacy concerns, model bias, lack of standardization, and high computational complexity prevent their adoption. The review finds significant research gaps, especially the lack of unified architectures, real-time explainability, and large-scale clinical validation. The work provides a systematic basis for future studies by describing the major challenges, innovation opportunities, and strategic paths to create integrated, understandable, and scalable intelligent healthcare ecosystems.

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