Quantum AI in Healthcare : Revolutionizing Diagnosis, Treatment and Drug Discovery
DOI:
https://doi.org/10.32628/IJSRST2411351Keywords:
Quantum AI, Healthcare, Personalized Medicine, Genomic Analysis, Drug discoveryAbstract
This paper explores the convergence of synthetic intelligence (AI) and quantum computing, unveiling its potential to revolutionize healthcare. By leveraging quantum mechanics' standards, the paper examines how AI may be amplified to gain breakthroughs in clinical diagnoses, personalized treatment plans, and accelerated drug discovery. The exploration delves into how quantum simulations can model complex organic methods at a molecular level, permitting the prediction of remedy interactions and the layout of medicine with unequalled precision. Additionally, the paper discusses the combination of quantum sensors with AI for more suitable clinical imaging, capable of detecting diffused abnormalities. The transformative ability of this synergy is addressed, emphasizing its function in ushering in a brand new technology of personalised medicinal drug and efficient drug improvement.
Downloads
References
J. A. Jones, M. Mosca, and R. H. Hansen, ‘‘Implementation of a quantum search algorithm on a quantum computer,’’ Nature, vol. 393, no. 6683, pp. 344–346, May 1998. DOI: https://doi.org/10.1038/30687
G. Benenti, G. Casati, D. Rossini, and G. Strini, Principles of Quantum Computation and Information: A Comprehensive Textbook. Singapore: World Scientific, 2019. DOI: https://doi.org/10.1142/10909
V. Dunjko and H. J. Briegel, ‘‘Machine learning & artificial intelligence in the quantum domain: A review of recent progress,’’ Rep. Prog. Phys., vol. 81, no. 7, Jul. 2018, Art. no. 074001. DOI: https://doi.org/10.1088/1361-6633/aab406
K. Kaushik and A. Kumar, ‘‘Demystifying quantum blockchain for healthcare,’’ Secur. Privacy, vol. 6, no. 3, p. e284, May 2023. DOI: https://doi.org/10.1002/spy2.284
C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, and L. Wossnig, ‘‘Quantum machine learning: A classical perspective,’’ Proc. Roy. Soc. A, Math., Phys. Eng. Sci., vol. 474, no. 2209, 2018, Art. no. 20170551. DOI: https://doi.org/10.1098/rspa.2017.0551
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, ‘‘Quantum machine learning,’’ Nature, vol. 549, no. 7671, pp. 195–202, 2017. DOI: https://doi.org/10.1038/nature23474
G. Cohen, S. Afshar, J. Tapson, and A. van Schaik, ‘‘EMNIST: Extending MNIST to handwritten letters,’’ in Proc. Int. Joint Conf. Neural Netw. (IJCNN), May 2017, pp. 2921–2926. DOI: https://doi.org/10.1109/IJCNN.2017.7966217
P. Benioff, ‘‘The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines,’’ J. Stat. Phys., vol. 22, no. 5, pp. 563–591, May 1980. DOI: https://doi.org/10.1007/BF01011339
A. Delgado et al., ‘‘Quantum computing for data analysis in high energy physics,’’ 2022, arXiv:2203.08805.
S. Lloyd, M. Mohseni, and P. Rebentrost, ‘‘Quantum algorithms for supervised and unsupervised machine learning,’’ 2013, arXiv:1307.0411.
S. C. Kak, ‘‘Quantum neural computing,’’ Adv. Imag. Electron Phys., vol. 94, pp. 259–313, Sep. 1995. DOI: https://doi.org/10.1016/S1076-5670(08)70147-2
A. A. Ezhov and D. Ventura, ‘‘Quantum neural networks,’’ in Future Directions for Intelligent Systems and Information Sciences: The Future of Speech and Image Technologies, Brain Computers, WWW, and Bioinformatics. Heidelberg, Germany: Springer, 2000, pp. 213–235. DOI: https://doi.org/10.1007/978-3-7908-1856-7_11
S. Y. Chen, S. Yoo, and Y. L. Fang, ‘‘Quantum long short-term memory,’’ in Proc. ICASSP - IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), May 2022, pp. 8622–8626 DOI: https://doi.org/10.1109/ICASSP43922.2022.9747369
A. EL Azzaoui, P. K. Sharma, and J. H. Park, ‘‘Blockchain-based delegated quantum cloud architecture for medical big data security,’’ J. Netw. Comput. Appl., vol. 198, Feb. 2022, Art. no. 103304. DOI: https://doi.org/10.1016/j.jnca.2021.103304
X. Yuan, C. Yang, Q. He, J. Chen, D. Yu, J. Li, S. Zhai, Z. Qin, K. Du, Z. Chu, and P. Qin, ‘‘Current and perspective diagnostic techniques for COVID-19,’’ ACS Infectious Diseases, vol. 6, no. 8, pp. 1998–2016, Aug. 2020. DOI: https://doi.org/10.1021/acsinfecdis.0c00365
S. Deshmukh, B. K. Behera, P. Mulay, E. A. Ahmed, S. Al-Kuwari, P. Tiwari, and A. Farouk, ‘‘Explainable quantum clustering method to model medical data,’’ Knowl.-Based Syst., vol. 267, May 2023, Art. no. 110413. DOI: https://doi.org/10.1016/j.knosys.2023.110413
S. Moradi, C. Brandner, C. Spielvogel, D. Krajnc, S. Hillmich, R. Wille, W. Drexler, and L. Papp, ‘‘Clinical data classification with noisy intermediate scale quantum computers,’’ Sci. Rep., vol. 12, no. 1, p. 1851, Feb. 2022. DOI: https://doi.org/10.1038/s41598-022-05971-9
J. Amin, M. Sharif, N. Gul, S. Kadry, and C. Chakraborty, ‘‘Quantum machine learning architecture for COVID-19 classification based on synthetic data generation using conditional adversarial neural network,’’ Cognit. Comput., vol. 14, no. 5, pp. 1677–1688, Sep. 2022. DOI: https://doi.org/10.1007/s12559-021-09926-6
M. Mahmud and S. Vassanelli, ‘‘Processing and analysis of multichannel extracellular neuronal signals: State-of-the-Art and challenges,’’ Frontiers Neurosci., vol. 10, p. 248, Jun. 2016. DOI: https://doi.org/10.3389/fnins.2016.00248
T. C. Major and J. M. Conrad, ‘‘A survey of brain computer interfaces and their applications,’’ in Proc. IEEE SOUTHEASTCON, Mar. 2014, pp. 1–8. DOI: https://doi.org/10.1109/SECON.2014.6950751
F. Arute, K. Arya, R. Babbush, D. Bacon, and J. C. Bardin, ‘‘Quantum supremacy using a programmable superconducting processor,’’ Nature, vol. 574, no. 7779, pp. 505–510, Oct. 7779.
M. Zinner, F. Dahlhausen, P. Boehme, J. Ehlers, L. Bieske, and L. Fehring, ‘‘Quantum computing’s potential for drug discovery: Early stage industry dynamics,’’ Drug Discovery Today, vol. 26, no. 7, pp. 1680–1688, Jul. 2021. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S1359644621002750 DOI: https://doi.org/10.1016/j.drudis.2021.06.003
S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin, and X. Yuan,‘‘Quantum computational chemistry,’’ Rev. Mod. Phys., vol. 92, Mar. 2020, Art. no. 015003, doi: 10.1103/RevModPhys.92.015003. DOI: https://doi.org/10.1103/RevModPhys.92.015003
S. Duela, A. Umamageswari, R. Prabavathi, P. Umapathy, and K. Raja, ‘‘Quantum assisted genetic algorithm for sequencing compatible amino acids in drug design,’’ in Proc. 3rd Int. Conf. Adv. Electr., Comput., Commun. Sustain. Technol. (ICAECT), Jan. 2023, pp. 1–7.
B. Lau, P. S. Emani, J. Chapman, L. Yao, T. Lam, P. Merrill, J. Warrell, M. B. Gerstein, and H. Y. K. Lam, ‘‘Insights from incorporating quantum computing into drug design workflows,’’ Bioinformatics, vol. 39, no. 1, Jan. 2023, Art. no. btac789. DOI: https://doi.org/10.1093/bioinformatics/btac789
H. Mustafa, S. N. Morapakula, P. Jain, and S. Ganguly, ‘‘Variational quantum algorithms for chemical simulation and drug discovery,’’ in Proc. Int. Conf. Trends Quantum Comput. Emerg. Bus. Technol. (TQCEBT), Oct. 2022, pp. 1–8. DOI: https://doi.org/10.1109/TQCEBT54229.2022.10041453
P. H. Wang, J.-H. Chen, Y.-Y. Yang, C. Lee, and Y. J. Tseng, ‘‘Recent advances in quantum computing for drug discovery and development,’’ IEEE Nanotechnol. Mag., vol. 17, no. 2, pp. 26–30, Apr. 2023. DOI: https://doi.org/10.1109/MNANO.2023.3249499
Y. Cao, J. Romero, and A. Aspuru-Guzik, ‘‘Potential of quantum computing for drug discovery,’’ IBM J. Res. Develop., vol. 62, no. 6, pp. 6:1–6:20, Nov. 2018. DOI: https://doi.org/10.1147/JRD.2018.2888987
T. S. Humble, H. Thapliyal, E. Muñoz-Coreas, F. A. Mohiyaddin, and R. S. Bennink, ‘‘Quantum computing circuits and devices,’’ IEEE Des. Test. IEEE Des. Test. Comput., vol. 36, no. 3, pp. 69–94, Jun. 2019. DOI: https://doi.org/10.1109/MDAT.2019.2907130
M. Swathi and B. Rudra, ‘‘Implementation of reversible logic gates with quantum gates,’’ in Proc. IEEE 11th Annu. Comput. Commun. Workshop Conf. (CCWC), Jan. 2021, pp. 1557–1563. DOI: https://doi.org/10.1109/CCWC51732.2021.9376060
P. K. Roy, ‘‘Quantum logic gates,’’ Tech. Rep., Aug. 2020.
L. Banchi, N. Pancotti, and S. Bose, ‘‘Quantum gate learning in qubit networks: Toffoli gate without time-dependent control,’’ NPJ Quantum Inf., vol. 2, no. 1, pp. 1–6, Jul. 2016. DOI: https://doi.org/10.1038/npjqi.2016.19
G. M. Morris and M. Lim-Wilby, ‘‘Molecular docking,’’ in Molecular Modeling of Proteins. Cham, Switzerland: Springer, 2008, pp. 365–382. DOI: https://doi.org/10.1007/978-1-59745-177-2_19
D. Gioia, M. Bertazzo, M. Recanatini, M. Masetti, and A. Cavalli,‘‘Dynamic docking: A paradigm shift in computational drug discovery,’’ Molecules, vol. 22, no. 11, p. 2029, Nov. 2017. DOI: https://doi.org/10.3390/molecules22112029
Jackson M, McAdams S. The future of quantum drug discovery. Available online: https://medium.com/cambridge-quantum-computing/the-future-of-quantum-drug-discovery-909aa5140bff (accessed on 6 December 2023).
Evers M, Heid A, Ostojic I. Pharma’s digital Rx: Quantum computing in drug research and development. Available online: https://www.mckinsey.com/industries/life-sciences/our-insights/pharmas-digital-rx-quantum-computing-in -drug-research-and-development (accessed on 6 December 2023).
Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discovery Today 2017; 22(11): 1680–1685. doi: 10.1016/j.drudis.2017.08.010 DOI: https://doi.org/10.1016/j.drudis.2017.08.010
Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 1970; 48(3): 443–453. doi: 10.1016/0022-2836(70)90057-4 DOI: https://doi.org/10.1016/0022-2836(70)90057-4
Smith TF, Waterman MS. Identification of common molecular subsequences. Journal of Molecular Biology 1981; 147(1): 195–197. doi: 10.1016/0022-2836(81)90087-5 DOI: https://doi.org/10.1016/0022-2836(81)90087-5
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022). AlphaFold, artificial intelligence (AI), and allostery. The Journal of Physical Chemistry B, 126(34), 6372-6383. DOI: https://doi.org/10.1021/acs.jpcb.2c04346
Ahmad, A., Hussain, H. K., Tanveer, H., Kiruthiga, T., & Gupta, K. (2023, February). The Intelligent Heart Rate Monitoring Model for Survivability Prediction of Cardiac Arrest Patients Using Deep Cardiac Learning Model. In 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) (pp. 376381). IEEE DOI: https://doi.org/10.1109/ICISCoIS56541.2023.10100413
Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., ... & Sony, H. T. (2023). Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences, 24(3), 2026. DOI: https://doi.org/10.3390/ijms24032026
Nova, K. (2023). AI-enabled water management systems: an analysis of system components and interdependencies for water conservation. Eigenpub Review of Science and Technology, 7(1), 105-124.
Hussain, H. K., Tariq, A., & Gill, A. Y. (2023). Role of AI in Cardiovascular Health Care; a Brief Overview. Journal of World Science, 2(4), 794-802.
E. Rieffel and W. Polak, ‘‘An introduction to quantum computing for nonphysicists,’’ ACM Comput. Surv., vol. 32, no. 3, pp. 300–335, Sep. 2000. DOI: https://doi.org/10.1145/367701.367709
T. Niedermaier, T. Gredner, S. Kuznia, B. Schttker, U. Mons, and H. Brenner, ‘‘Vitamin D supplementation to the older adult population in Germany has the cost saving potential of preventing almost 30 000 cancer deaths per year,’’ Mol. Oncol., vol. 15, no. 8, pp. 1986–1994, 2021. DOI: https://doi.org/10.1002/1878-0261.12924
D. E. Newman-Toker, Z. Wang, Y. Zhu, N. Nassery, A. S. Saber Tehrani, A. C. Schaffer, C. W. Yu-Moe, G. D. Clemens, M. Fanai, and D. Siegal, ‘‘Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: Toward a national incidence estimate using the ‘big three,’’’ Diagnosis, vol. 8, no. 1, pp. 67–84, Feb. 2021. DOI: https://doi.org/10.1515/dx-2019-0104
B. M. Boghosian and W. Taylor, ‘‘Simulating quantum mechanics on a quantum computer,’’ Phys. D, Nonlinear Phenomena, vol. 120, nos. 1–2, pp. 30–42, Sep. 1998. DOI: https://doi.org/10.1016/S0167-2789(98)00042-6
Liu CY, Goan HS. Hybrid gate-based and annealing quantum computing for large-size Ising problems. Available online: https://arxiv.org/abs/2208.03283 (accessed on 6 December 2023).
S. K. Sood and Pooja, ‘‘Quantum computing review: A decade of research,’’ IEEE Trans. Eng. Manag., vol. 71, pp. 6662–6676, 2024, doi: 10.1109/TEM.2023.3284689. [51]S. Hussain, I. Mubeen, N. Ullah, S. S. U. D. Shah, B. A. Khan, M. Zahoor, R. Ullah, F. A. Khan, and M. A. Sultan, ‘‘Modern diagnostic imaging technique applications and risk factors in the medical field: A review,’’ BioMed Res. Int., vol. 2022, pp. 1–19, Jun. 2022. DOI: https://doi.org/10.1155/2022/5164970
Nova, K. (2023). Generative AI in healthcare: advancements in electronic health records, facilitating medical languages, and personalized patient care. Journal of Advanced Analytics in Healthcare Management, 7(1), 115-131[53]E. H. Houssein, Z. Abohashima, M. Elhoseny, and W. M. Mohamed, ‘‘Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images,’’ J. Comput. Des. Eng., vol. 9, no. 2, pp. 343–363, Feb. 2022. DOI: https://doi.org/10.1093/jcde/qwac003
A. Zeilinger, ‘‘Quantum entanglement and information,’’ in Proc. Quantum Electron. Laser Sci. Conf. (QELS). Tech. Dig., vol. 10, May 2000, p. 163.
S. Ray, ‘‘A quick review of machine learning algorithms,’’ in Proc. Int. Conf. Mach. Learn., Big Data, Cloud Parallel Comput. (COMITCon), Feb. 2019, pp. 35–39. DOI: https://doi.org/10.1109/COMITCon.2019.8862451
Awad, A., Trenfield, S. J., Goyanes, A., Gaisford, S., & Basit, A. W. (2018). Reshaping drug development using 3D printing. Drug discovery today, 23(8), 1547-1555 DOI: https://doi.org/10.1016/j.drudis.2018.05.025
Jackson M. The future of quantum drug discovery. Available online:https://medium.com/cambridge-quantum-computing/the-future-of-quantum-drug-discovery-909aa5140bff (accessed on 6 December 2023).
A. Montanaro, ‘‘Quantum algorithms: An overview,’’ NPJ Quantum Inf., vol. 2, no. 1, pp. 1–8, Jan. 2016. DOI: https://doi.org/10.1038/npjqi.2015.23
Nova, K., Umaamaheshvari, A., Jacob, S. S., Banu, G., Balaji, M. S. P., & Srithar, S. (2023). Floyd–Warshalls algorithm and modified advanced encryption standard for secured communication in VANET. Measurement: Sensors, 27, 100796. DOI: https://doi.org/10.1016/j.measen.2023.100796
Nova, K. (2019). The Art of Elasticity and Scalability of Modern Cloud Computing World for Automation. American Journal of Computer Architecture, 6(1), 1-6.
Wang D, Liu S, Warrell J, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 2018; 362(6420): eaat8464. doi: 10.1126/science.aat8464 DOI: https://doi.org/10.1126/science.aat8464
Ward LD, Kellis M. Interpreting noncoding genetic variation in complex traits and human disease. Nature Biotechnology 2012; 30(11): 1095–1106. doi: 10.1038/nbt.2422 DOI: https://doi.org/10.1038/nbt.2422
Gusev A, Ko A, Shi H, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics 2016; 48(3): 245–252. doi: 10.1038/ng.3506 DOI: https://doi.org/10.1038/ng.3506
Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nature Reviews Genetics 2017; 18(2): 117–127. doi: 10.1038/nrg.2016.142 DOI: https://doi.org/10.1038/nrg.2016.142
Mirage News. (Jan. 2024). GENCI/CEA, FZJ, PASQAL Achieve Major Hybrid Computing Milestone. Accessed: Jan. 20, 2021. [Online]. Available: https://www.miragenews.com/gencicea-fzj-pasqal-achieve-majorhybrid-1120388
Malandraki-Miller, S., & Riley, P. R. (2021). Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discovery Today, 26(4), 887-901. DOI: https://doi.org/10.1016/j.drudis.2021.01.013
Mulligan VK, Melo H, Merritt HI, et al. Designing peptides on a quantum computer. Available online: https://www.biorxiv.org/content/10.1101/752485v2.full.pdf (accessed on 6 December 2023).
R. E. Amaro and A. J. Mulholland, ‘‘Multiscale methods in drug design bridge chemical and biological complexity in the search for cures,’’ Nature Rev. Chem., vol. 2, no. 4, p. 0148, Apr. 2018. DOI: https://doi.org/10.1038/s41570-018-0148
N. Schaduangrat, S. Lampa, S. Simeon, M. P. Gleeson, O. Spjuth, and C. Nantasenamat, ‘‘Towards reproducible computational drug discovery,’’Cheminformatics, vol. 12, no. 1, pp. 1–30, Dec. 2020. DOI: https://doi.org/10.1186/s13321-020-0408-x
N. Alsharabi, T. Shahwar, A. U. Rehman, and Y. Alharbi, ‘‘Implementing magnetic resonance imaging brain disorder classification via AlexNet–Quantum learning,’’ Mathematics, vol. 11, no. 2, p. 376, Jan. 2023. DOI: https://doi.org/10.3390/math11020376
C. Gorgulla, A. Jayaraj, K. Fackeldey, and H. Arthanari, ‘‘Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches,’’ Current Opinion Chem. Biol., vol. 69, Aug. 2022, Art. no. 102156. DOI: https://doi.org/10.1016/j.cbpa.2022.102156
F. Gentile, J. C. Yaacoub, J. Gleave, M. Fernandez, A.-T. Ton, F. Ban, A. Stern, and A. Cherkasov, ‘‘Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking,’’ Nature Protocols, vol. 17, no. 3, pp. 672–697, Mar. 2022. DOI: https://doi.org/10.1038/s41596-021-00659-2
F. Bova, A. Goldfarb, and R. G. Melko, ‘‘Commercial applications of quantum computing,’’ EPJ Quantum Technol., vol. 8, no. 1, p. 2, Dec. 2021. DOI: https://doi.org/10.1140/epjqt/s40507-021-00091-1
P. B. Upama, M. J. H. Faruk, M. Nazim, M. Masum, H. Shahriar, G. Uddin, S. Barzanjeh, S. I. Ahamed, and A. Rahman, ‘‘Evolution of quantum computing: A systematic survey on the use of quantum computing tools,’’ in Proc. IEEE 46th Annu. Comput., Softw., Appl. Conf. (COMPSAC), Jun. 2022, pp. 520–529. DOI: https://doi.org/10.1109/COMPSAC54236.2022.00096
Jamal, A. (2023). Antibiotics in Contemporary Medicine: Advances, Obstacles, and the Future. BULLET : Jurnal Multidisiplin Ilmu, 2(2), 548-557.
L. F. Liu, ‘‘DNA topoisomerase poisons as antitumor drugs,’’ Annu. Rev. Biochem., vol. 58, no. 1, pp. 351–375, Jun. 1989. DOI: https://doi.org/10.1146/annurev.bi.58.070189.002031
Niazi, S. K., & Mariam, Z. (2023). Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review. International Journal of Molecular Sciences, 24(14), 11488. DOI: https://doi.org/10.3390/ijms241411488
C. Wohlin, ‘‘Guidelines for snowballing in systematic literature studies and a replication in software engineering,’’ in Proc. 18th Int. Conf. Eval. Assessment Softw. Eng., May 2014, pp. 1–10. DOI: https://doi.org/10.1145/2601248.2601268
H. Gupta, H. Varshney, T. K. Sharma, N. Pachauri, and O. P. Verma, ‘‘Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction,’’ Complex Intell. Syst., vol. 8, no. 4, pp. 3073–3087, Aug. 2022. DOI: https://doi.org/10.1007/s40747-021-00398-7
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
