Quantum Computing for Healthcare AI: Adaptive Diagnosis Models that Evolve with Patient Data and Medical Knowledge
Keywords:
Quantum Computing, Artificial Intelligence, Healthcare, Adaptive Models, Diagnosis, Personalized Medicine, Quantum Machine Learning, Deep Learning, Medical KnowledgeAbstract
The integration of quantum computing and artificial intelligence (AI) holds transformative potential for healthcare by enabling adaptive diagnostic models that evolve with dynamic patient data and expanding medical knowledge. Leveraging quantum principles such as superposition and entanglement allows these models to efficiently process complex, high-dimensional healthcare datasets, improving precision in disease diagnosis and personalized treatment strategies. The proposed hybrid architecture combines quantum machine learning algorithms with classical AI components for interpretability and continuous refinement through real-time data ingestion, mitigating model drift and maintaining clinical relevance. This approach demonstrates significant promise in enhancing diagnostic accuracy and fostering intelligent healthcare solutions responsive to individual patient needs and medical advances [1]– [3].
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[1].R. Idzikowski, M. A. Kucharski, K. Pempera, and M. Jaroszczuk, "A survey on quantum machine learning applications in medicine and healthcare," Applied Sciences, vol. 16, p. 1630, 2026. https://doi.org/10.3390/app16031630
[2] T. O. Fatunmbi, "Quantum computing and healthcare AI," 2023.
[3] H. Ali, "Quantum computing and AI in healthcare: Accelerating complex biological simulations, genomic data processing, and drug discovery innovations," World Journal of Advanced Research and Reviews, vol. 20, pp. 1466–1484, 2023. https://doi.org/10.30574/wjarr.2023.20.2.2325
[4] V. Nizam and A. Aslekar, "Challenges of applying AI in healthcare in India," Journal of Pharmaceutical Research International, pp. 203–209, 2021. https://doi.org/10.9734/jpri/2021/v33i36b31969
[5] J. C. L. Chow, "Quantum computing and machine learning in medical decision-making: A comprehensive review," Algorithms, vol. 18, no. 3, p. 156, 2025. https://doi.org/10.3390/a18030156
[6] U. Ullah and B. García-Zapirain, "Quantum machine learning revolution in healthcare: A systematic review of emerging perspectives and applications," IEEE Access, vol. 12, pp. 11423–11450, 2024. https://doi.org/10.1109/access.2024.3353461
[7] S. Rani, P. K. Pareek, J. Kaur, M. Chauhan, and P. Bhambri, "Quantum machine learning in healthcare: Developments and challenges," in 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), pp. 1–7. https://doi.org/10.1109/icicacs57338.2023.10100075
[8] S. N. P. Kumar, "Quantum-enhanced AI decision systems: Architectural approaches for cloud-based machine learning applications," Zenodo (CERN European Organization for Nuclear Research), 2025. https://doi.org/10.5281/zenodo.16801355
[9] N. Banerjee and K. Chatterjee, "Quantum AI in healthcare: Revolutionizing diagnosis, treatment and drug discovery," International Journal of Scientific Research in Science and Technology, vol. 11, pp. 815–836, 2024. https://doi.org/10.32628/ijsrst2411351
[10] T. T. Njei, T. D. Oluyemo, O. I. Owoyomi, V. R. Olatunde, A. Odetoran, and G. J. Ilori, "Quantum computing and AI in drug discovery and public health: Accelerating breakthroughs for global healthcare challenge," Zenodo, 2025. https://doi.org/10.5281/zenodo.17263817
[11] Z. Zhang, "From algorithm to impact: How AI algorithms shape personalized treatment strategies in modern healthcare," pp. 1165–1170, 2025. https://doi.org/10.1145/3785706.3785889
[12] A. Hatay et al., "Quantum AI for adaptive healthcare," 2025.
[13] R. Ur Rasool et al., "Quantum-classical hybrid systems for healthcare," 2023.
[14] Z. Zhang, "From algorithm to impact: How AI algorithms shape personalized treatment strategies in modern healthcare," 2025.
[15] A. Callahan et al., "Standing on FURM ground — A framework for evaluating fair, useful, and reliable AI models in healthcare systems," 2024. https://doi.org/10.48550/arxiv.2403.07911
[16] S. G. A, K. M. Monica, R. Prabha, L. Prinslin, and R. Elavarasi, "Quantum AI: A cognitive machine learning technique based on nurturing food security sustainability predictive analysis for life science - bioengineering in healthcare," BIO Web of Conferences, vol. 172, p. 02002, 2025. https://doi.org/10.1051/bioconf/202517202002
[17] R. Mangharamani and G. Raj, "Quantum-driven predictive healthcare: Next-gen risk modeling & AI-powered decision support," International Journal of Research in All Subjects in Multi Languages, vol. 13, pp. 140–151, 2025. https://doi.org/10.63345/ijrsml.v13.i3.9
[18] D. R. Kale, P. Jain, T. Deshpande, S. A. Mujawar, S. G. Sapate, and S. A. Hirve, "Design and evaluation of a quantum-enabled explainable AI system for disease risk prediction in healthcare," pp. 1–6, 2025. https://doi.org/10.1109/icbds67396.2025.11376732
[19] M. R. Pulicharla, "Hybrid quantum-classical machine learning models: Powering the future of AI," Journal of Science & Technology, vol. 4, pp. 40–65, 2023. https://doi.org/10.55662/jst.2023.4102
[20] H. Billah, "Performance analysis of a variational quantum model for healthcare data," 2025. https://doi.org/10.5281/zenodo.16858485
[21] "Hybrid quantum-classical algorithms: Variational quantum eigensolver (VQE) algorithm," 2020. https://www.semanticscholar.org/paper/225074206
[22] D. Gunlycke, C. S. Hellberg, and J. Stenger, "Cascaded variational quantum eigensolver algorithm," Physical Review Research, vol. 6, 2024. https://doi.org/10.1103/physrevresearch.6.013238
[23] R. Shahbazian, I. Trubitsyna, and S. L. Mirtaheri, "Joint explainable and fair AI in healthcare," BMC Medical Informatics and Decision Making, vol. 25, p. 403, 2025. https://doi.org/10.1186/s12911-025-03233-y
[24] D. Claudino, J. Wright, A. J. McCaskey, and T. S. Humble, "Benchmarking adaptive variational quantum eigensolvers," Frontiers in Chemistry, vol. 8, p. 606863, 2020. https://doi.org/10.3389/fchem.2020.606863
[25] R. Idzikowski, M. A. Kucharski, K. Pempera, and M. Jaroszczuk, "A survey on quantum machine learning applications in medicine and healthcare," Applied Sciences, vol. 16, p. 1630, 2026. https://doi.org/10.3390/app16031630
[26] R. Sheikholeslami et al., "Adaptive QML model evaluation," 2025.
[27] Z. Lan and W. Liang, "Amplitude reordering accelerates the adaptive variational quantum eigensolver algorithms," Journal of Chemical Theory and Computation, vol. 18, pp. 5267–5275, 2022. https://doi.org/10.1021/acs.jctc.2c00403
[28] Z. Lan and W. Liang, "Amplitude reordering accelerates the adaptive variational quantum eigensolver algorithms," ChemRxiv, 2022. https://doi.org/10.26434/chemrxiv-2022-ttwkd
[29] S. Rawas and D. AlSaeed, "Federated quantum learning for healthcare," 2026.
[30] A. Marengo and V. Santamato, "Quantum algorithms and complexity in healthcare applications: A systematic review with machine learning-optimized analysis," Frontiers in Computer Science, vol. 7, 2025. https://doi.org/10.3389/fcomp.2025.1584114.
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