An Empirical Study and Analysis of Artificial Intelligence, Machine Learning, and Big Data for Crypto Healthcare Industries

Introduction

The intersection of crypto technology and healthcare is creating exciting opportunities for innovation and advancement (Habib et al 2022). In this chapter, the role of AI, ML & big data in crypto healthcare is investigated and highlighting their potential to reshape how healthcare is delivered and accessed.

AI, ML & Big Data plays a significant roles in the healthcare industry, including in the context of crypto healthcare, which often involves the use of blockchain technology for securing and managing healthcare data (Dimitrov et al. 2019). AI gives a pivotal part in crypto healthcare through leveraging the capacities to enhance security, privacy & efficiency in healthcare systems that utilize blockchain technology and cryptocurrencies. Cryptography: AI can assist in developing and improving cryptographic techniques used in blockchain-based healthcare systems, making them more resilient to attacks and ensuring the confidentiality of sensitive patient data (Shi et al. 202). Machine Learning (ML) has several important roles in crypto healthcare, where blockchain technology and cryptocurrencies are utilized to improve security, privacy, and efficiency in healthcare systems. ML techniques could be adopted to various aspects of crypto healthcare to enhance data analysis, security, and patient care (Mohd Javaid et al. 2022).

ML techniques will examine huge number of healthcare information stored on blockchain networks, extracting valuable insights about patient health, treatment outcomes, and disease trends. This information can aid in making data-driven decisions and improving patient care. ML models used to forecast the type of disease, patients re-entry and patient treatment depends upon the history of patient information, helping medical care providers allot properties more effectively and provide proactive care. ML techniques also examine the people health information and genetic information to recommend personalized treatment plans and medication regimens. This approach improves treatment efficacy and reduces adverse effects. ML can identify unusual patterns and anomalies in blockchain transactions related to healthcare, helping to detect fraudulent activities such as insurance fraud or unauthorized access to patient records. ML can assist in improving the security of blockchain networks by continuously monitoring for suspicious activities and potential security breaches (Mohammed et al. 2023). It can also enhance cryptographic techniques used in healthcare blockchain systems. ML-powered algorithms can be used to automate and enforce data privacy policies on blockchain networks, ensuring that patient data is only accessible to authorized individuals or entities.

In crypto healthcare, ML complements blockchain technology by providing advanced data analysis, security, and automation capabilities, ultimately leading to improved patient care, enhanced data privacy, and more efficient healthcare processes.

Here's how AI, ML and Big Data are contributing to crypto healthcare:

• 1.

  Data Security and Privacy:

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    Blockchain Technology: It provides a safe and secure technique to deposit & manage healthcare information. It also guarantees data protection and privacy by enabling patients to have control over their health records while allowing healthcare providers access as needed.

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    Cryptography: AI and ML can be used to enhance the encryption and decryption processes in blockchain systems, making data even more secure.

• 2.

  Data Management:

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    Big Data Analytics: With the vast amounts of data generated in healthcare, Big Data analytics can help extract valuable insights from patient records, clinical trials, and research data.

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    AI-driven Data Processing: AI algorithms can assist in organizing and categorizing large datasets, making them more manageable and accessible.

• 3.

  Predictive Analytics:

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    ML techniques provides the various stage of checkpoints to investigate the people information to forecast disease outbreaks, patient details, and treatment effectiveness. This can help healthcare organizations make informed decisions and allocate resources efficiently.

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    AI-Powered Diagnostics: AI can be used for image analysis, such as interpreting medical images like X-rays and MRIs, aiding in the early detection of diseases.

• 4.

  Personalized Medicine: