Jul 2026· International Journal of Innovative Research in Engineering· pp. 21· 0 citations
TL;DR
The review suggests that quantum-assisted XAI is in its infancy but has immense potential to design intelligent, transparent, and efficient healthcare systems that can help promote reliable disease prediction and precision therapy.
Abstract
The smart healthcare systems are transforming disease diagnosis by using robust computational technology, enabling timely, accurate, and customized clinical decision-making. Explainable Artificial Intelligence (XAI) has been introduced as a promising way to enhance the transparency, interpretability, and trustworthiness of artificial intelligence (AI) models, while Quantum Computing (QC) offers unmatched computational capacity to tackle complex healthcare optimization and machine learning (ML) problems. The convergence of XAI and QC can also considerably improve disease prediction through the integration of interpretable decision support and rapid data processing and optimization. This paper provides a short literature survey on the roles of XAI and QC in smart healthcare systems for disease diagnosis. It includes advanced AI methods like deep learning (DL) and machine learning (ML) models, along with top QC tools such as Qiskit, PennyLane, Cirq, and TensorFlow Quantum. It encompasses explainability techniques such as SHAP, LIME, and Grad-CAM, which are regarded as some of the most sophisticated in the world. The survey also discusses the use of these technologies in the prediction and diagnosis of diseases such as cancer, cardiovascular diseases, diabetes, neurological diseases, and infectious diseases. The paper provides a comparative examination of existing approaches, applications in the real world of healthcare, issues that are currently being faced, and future research priorities. The review suggests that quantum-assisted XAI is in its infancy but has immense potential to design intelligent, transparent, and efficient healthcare systems that can help promote reliable disease prediction and precision therapy. The insights provided in this study present a comprehensive understanding to the researchers and healthcare practitioners about emerging trends, research gaps, and feasible directions for future smart healthcare applications.
: Heart disease continues to be one of the most serious global health challenges, affecting people’s quality of life and placing a heavy burden on health-care systems. To improve early diagnosis and support better patient outcomes, this study examines a computational approach that brings together methods from machine learning, deep learning, and emerging quantum computing. Traditional machine learning models offer interpretable insights when working with structured clinical data, while deep learning techniques are well suited for identifying subtle patterns in complex sources such as ECG signals and cardiac imaging. In addition, quantum computing methods such as quantum support vector machines and variation quantum circuits show promise in exploring features more efficiently and speeding up optimization, potentially overcoming some of the limitations of classical computation. Early comparative results suggest that, when adequate quantum resources are available, hybrid quantum classical models can shorten training time and improve prediction accuracy. Overall, this integrated approach highlights how advanced computational techniques can support more effective health-care delivery, strengthen preventive care, and enable real-time, scalable detection of cardiac diseases.
M. Sathya, K. Balasubramanian· Proceedings of the 1st Inter...· 0 citations
A reproducible experimental approach to developing and evaluating explainable AI systems for healthcare analytics that integrates the steps of data preprocessing, predictive modeling, interpretation generation, and evaluation into one seamless workflow that can be applied to both structured clinical data and medical imaging datasets.
Yashwant Dongre, Deepali A. Godse, Prawit Chumchu et al.· Journal of Visualized Experi...· 0 citations
Highlights
Artificial intelligence is transforming cardiology by enabling more accurate diagnosis, personalized therapy, and prediction of cardiovascular complications.
The study provides a comprehensive systematization of current approaches to machine learning, neural networks, and big data analytics in clinical cardiology.
Key directions for integrating AI into Russian healthcare are highlighted, considering ethical, legal, and organizational aspects.
Abstract
Modern cardiology is undergoing a rapid digital transformation driven by artificial intelligence (AI). The application of machine learning and deep learning algorithms provides unprecedented opportunities for diagnosis, monitoring, and prediction of cardiovascular diseases (CVDs). This review summarizes current evidence on the integration of AI across key domains of cardiovascular care–from data acquisition and analysis to personalized treatment optimization. The article highlights successful applications of AI in electrocardiogram interpretation, cardiovascular imaging, hemodynamic assessment, and prediction of heart failure exacerbations. Particular attention is paid to ethical, legal, and organizational aspects of AI implementation, including transparency, data security, and clinical accountability. International and national frameworks, such as the EU Artificial Intelligence Act, GDPR, and Russian federal regulations, are discussed as foundations for safe and equitable adoption of AI in healthcare. The review also outlines the Russian Federation’s initiatives in digital health transformation, including the development of domestic diagnostic algorithms and unified medical data repositories. Future perspectives include the use of quantum computing, emotional AI, and integration of digital competencies into medical education. Artificial intelligence is viewed as a transformative tool to enhance diagnostic accuracy, treatment efficiency, and preventive strategies in cardiology, provided that human oversight and clinical validation remain central.
Unknown authors· Complex Issues of Cardiovasc...· 0 citations
Artificial intelligence (AI) is becoming an important technology in modern healthcare because of its ability to analyze large volumes of clinical, biomedical, and patient-generated data. AI-based systems are being applied in disease diagnosis, medical imaging, drug discovery, personalized medicine, clinical decision support, remote monitoring, and healthcare administration. This review examines recent trends in the use of AI in healthcare by analyzing six influential studies published between 2017 and 2024. The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care. However, challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation. The review concludes that AI should be implemented as a supportive tool under appropriate clinical supervision, ethical standards, and regulatory frameworks.
Keywords: artificial intelligence; machine learning; deep learning; explainable AI; Medical Imaging; precision medicine
Unknown authors· International Journal of Tec...· 0 citations
It is concluded that explainable artificial intelligence improves trust, reliability, and accountability in healthcare systems and is a prerequisite for successful integration of intelligent technologies into clinical practice.
Riya Jacob K· International Journal of Tec...· 0 citations
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