Abstract
For a healthcare organization, it is very difficult to satisfy the growing challenges and cost and provide good quality care. But nowadays clinical decision support system becomes an essential tool for a healthcare organization to help healthcare experts enhance the treatment process and advance healthcare services. Clinical decision support system supports collaborative treatment to enhance medical services. In a collaborative treatment service, the patient’s health records are shared by different healthcare experts. All the patient health data are maintained by an electronic health records system. Electronic health records have very sensitive and patient’s private information so sharing electronic health records is a very challenging task. Some downsides of collaborative treatment are privacy and lack of confidence among contributors like a patient, doctors, radiologist, hospitals, and insurance organization. Blockchain which is known as distributed ledger technology and has a secured architecture framework can be used to enhance the healthcare organization. Blockchain with artificial intelligence has a great potential in helping healthcare traders tackle major healthcare issues and challenges. In this chapter, we discussed how artificial intelligence and blockchain as a powerful pair can transform the healthcare sector. We also discussed the model, challenges, and application in clinical decision support tools.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abhimanyu, S. A. (2019). The impact of artificial intelligence in medicine on the future role of the physician. Peer Journal, 7, e7702.
Akhlaq, A., Sheikh, A., & Pagliari, C. (2016). Defining health information exchange: Scoping review of published definitions. Journal of Innovation in Health Informatics, 23(4), 684–764. https://doi.org/10.14236/jhi.v23i4.838
Amisha, P. M., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328–2331.
Arnott, D., Pervan, G., O’Donnell, P., & Dodson, G. (2009). An analysis of decision support systems research: Preliminary results. Decision support in an uncertain and complex world: The IFIP TC8/WG8.3 international conference. pp. 25–38.
Bresnick, J. (2018). Top 12 ways artificial intelligence will impact healthcare. https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare
Chen, H. S., Jarrell, J. T., Carpenter, K. A., Cohen, D. S., & Huang, X. (2019). Blockchain in healthcare: A patient-centered model. Biomedical Journal of Scientific & Technical Research, 20(3), 15017–15022.
Cruz, J. A., & Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer Informatics, 2, 59–77. https://doi.org/10.1038/scientificamerican0519
Dawes, T. J. W., de Marvao, A., Shi, W., et al. (2017). Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: A cardiac MR imaging study. Radiology, 283, 381–390.
Fan, K., Wang, S., Ren, Y., et al. (2018). MedBlock: Efficient and secure medical data sharing via Blockchain. Journal of Medical Systems, 42, 136. https://doi.org/10.1007/s10916-018-0993-7
Gwyneth Iredale. (2020a). Lockchain definition: Everything you need to know.
Gwyneth Iredale. (2020b). 6 key Blockchain features you need to know now. https://101blockchains.com/introduction-to-blockchain-features/
https://www.predictiveanalyticstoday.com/what-is-ai-based-medical-imaging/
Jiang, F., Jiang, Y., Zhi, H., et al. (2017a). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2, e000101. https://doi.org/10.1136/svn-2017-000101
Jiang, Y., Qiu, B., Xu, C., & Li, C. (2017b). The research of clinical decision support system based on three-layer knowledge base model. Journal of Healthcare Engineering, 2017, 6535286. https://doi.org/10.1155/2017/6535286
Jiang, S., Cao, J., Wu, H., Yang, Y., Ma, M., He, J. (2018a). BlocHIE: A BLOCkchain-based platform for healthcare information exchange 978-1-5386-4705-9/18/$31.00 ©2018 IEEE. https://doi.org/10.1109/SMARTCOMP.2018.00073
Jiang, S. et al. (2018b). BlocHIE: A BLOCkchain-Based Platform for Healthcare Information Exchange. 2018 IEEE International Conference on Smart Computing (SMARTCOMP) (2018): 49–56.
Johnson, K. W., Soto, J. T., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018). Artificial intelligence in cardiology. Journals of the American College of Cardiology, 71(23), 2668–2679.
Kandhasamy, J. P., et al. (2019). Diagnosis of diabetic retinopathy using multi level set segmentation algorithm with feature extraction using svm with selective features. Multimedia Tools and Applications, 1–16.
Kent, J. (2020). Artificial intelligence tool diagnoses Alzheimer’s with 95% accuracy. https://healthitanalytics.com/news/artificial-intelligence-tool-diagnoses-alzheimers-with-95-accuracy
Krawiec, R.J., Housman, D., White, M., Filipova, M., Quarre, F., Barr, D., Nesbitt, A., Fedosova, K., Killmeyer, J., Israel, A., Tsai, L. (2016, August). Blockchain: Opportunities for health care, pp. 1–16
Li, J., Huang, J., Zheng, L., & Li, X. (2020). Application of artificial intelligence in diabetes education and management: Present status and promising prospect. Frontiers in Public Health, 8, 173.
Midhun, P., Rohith, R. N., John, T., Aby Abahai, T. (2019). Blochie: Blockchain based electronic health record, 2019. IJRTI, Volume 4, Issue 8, ISSN: 2456–3315.
Mishra, S. G., Takke, A., Suryavanshi, S. V., & Oza, M. J. (2017). Role of artificial intelligence in health care. Biochemical Journal, 11(5), 1–14.
Molero, I.. (2016). The industrial revolution of the Internet, https://ecommerceguider.com/history-of-blockchain/
Moving Towards web 3.0 Using Blockchain as Core Tech, Shahid Shaikh/16 Apr 2019/Blockchain /Web (history image)
Musleh, A. S., Yao, G., & Muyeen, S. M. (2019). Blockchain applications in smart grid – Review and frameworks. IEEE, XX, 1–13.
Pearlman, J. (2013). Clinical decision support systems for management decision making of cardiovascular diseases. https://pharmaceuticalintelligence.com/2013/05/04/cardiovascular-diseases-decision-support-systems-for-disease-management-decision-making/.
RodMcCullom. (2019). Alzheimer’s AI. Scientific American, 320, 5–20. https://doi.org/10.1038/scientificamerican0519-20
Shae, Z., & Tsai, J.J.. (2017). On the design of a blockchain platform for clinical trial and precision medicine. ICDCS. IEEE, 2017, pp. 1972–1980
Shaikh, F., et al. (2020). Artificial intelligence-based clinical decision support systems using advanced medical imaging & radiomics. Current Problems in Diagnostic Radiology. https://doi.org/10.1067/j.cpradiol.2020.05.006
Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association: JAMIA, 8(6), 527–534. https://doi.org/10.1136/jamia.2001.0080527
Siyal, A. A., Junejo, A. Z., Zawish, M., Ahmed, K., Khalil, A., & Soursou, G. (2019). Applications of Blockchain technology in medicine and healthcare: Challenges and future perspectives. Cryptography, 3, 3. https://doi.org/10.3390/cryptography3010003. www.mdpi.com/journal/cryptography
Sutton, R. T., Pincock, D., Baumgart, D. C., et al. (2020a). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17. https://doi.org/10.1038/s41746-020-0221-y
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020b). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17.
Thomas Davenport, A., & Ravi Kalakota, B. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.
Wasylewicz, A.T.M., & Scheepers-Hoeks, A.M.J.W. (2019). Clinical decision support systems, pp. 153–169, ISBN: 978-3-319-99712-4.
Xia, Q., Sifah, E. B., Asamoah, K. O., Gao, J., Du, X., & Guizani, M. (2017). Medshare: Trust-less medical data sharing among cloud service providers via blockchain. IEEE Access, 5, 14757–14767.
Yaga, D., Mell, P., Roby, N., Scarfone, K. (2018). Blockchain technology overview, https://doi.org/10.6028/NIST.IR.8202
Yang, Y., Zhang, J.-W., Zang, G.-Y., & Pu, J. (2019). The primary use of artificial intelligence in cardiovascular diseases: What kind of potential role does artificial intelligence play in future medicine? Journal of Geriatric Cardiology, 16(8), 585–591.
Yoon, H.-J. (2019). Blockchain technology and healthcare. Healthcare Informatics Research, 25(2), 59–60.
Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016). Healthcare data gateways: Found healthcare intelligence on Blockchain with novel privacy risk control. Journal of Medical Systems, 40(10), 218. https://doi.org/10.1007/s10916-016-0574-6. Epub 2016 Aug 26.
Zhang, P., White, J., Schmidt, D. C., Gunther, L., & Trent Rosenbloom, S. (2018). FHIRChain: Applying Blockchain to securely and Scalably share clinical data. Computational and Structural Biotechnology Journal, 16, 267–278.
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of Blockchain technology: Architecture, consensus, and future trends. 2017 IEEE 6th international congress on big data, 557–564
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Vijayalakshmi, S., Savita, Gayathri, S.P., Janarthanan, S. (2021). Blockchain Security for Artificial Intelligence-Based Clinical Decision Support Tool. In: Kumar, R., Wang, Y., Poongodi, T., Imoize, A.L. (eds) Internet of Things, Artificial Intelligence and Blockchain Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-74150-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-74150-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-74149-5
Online ISBN: 978-3-030-74150-1
eBook Packages: Computer ScienceComputer Science (R0)