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              <text>Blockchain security for artificial intelligence-based clinical decision support tool</text>
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              <text>Alzheimer; Artificial intelligence; Blockchain; Cancer; Cardiovascular; Clinical decision support system; Cohesic; Electronic health records; Healthcare security; Radiological tools</text>
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              <text>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.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved.</text>
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              <text>Vijayalakshmi S.; Savita; Gayathri S.P.; Janarthanan S.</text>
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              <text>Internet of Things, Artificial Intelligence and Blockchain Technology, pp. 209-240.</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-3-030-74150-1_10" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-030-74150-1_10&lt;/a&gt;
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              <text>ISBN: 978-303074150-1; 978-303074149-5</text>
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              <text>Vijayalakshmi S., Department of Data Science, Christ University, Pune Lavasa, India; Savita, School of Computing Science and Engineering, Galgotias University, Greater Noida, Delhi-NCR, India; Gayathri S.P., The Gandhigram Rural Institute (Deemed to be University), Dindigul, Tamilnadu, India; Janarthanan S., School of Computing Science and Engineering, Galgotias University, Greater Noida, Delhi-NCR, India</text>
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