Blockchain-assisted cyber security in medical things using artificial intelligence

The Internet of Medical Things (IoMT) significantly impacts our healthcare system because it allows us to track and verify patient medical data before storing it in the cloud for future use. A rapidly expanding platform like IoMT requires high security to keep all data safe. The patient's prescription history and other sensitive information must be encrypted and managed with great care. Nevertheless, it is challenging to determine what data uses are acceptable while protecting patient privacy and security. Understanding the limits of current technologies and envisioning future research paths is crucial for establishing a safe and reliable data environment. An untrustworthy person can communicate with a trustworthy person via blockchain, a decentralized digital ledger that allows for end-to-end communication. Therefore, this research suggests that the healthcare industry with blockchain-integrated cyber-security based on artificial intelligence (BICS-AI) in medical care to preserve medical-related things. Blockchain applications have the potential to consistently identify the most severe, potentially life-threatening mistakes in the medical field. The use of blockchain for decentralized data protection helps to protect patient health records from compromise. With the help of an access control provider (ACP), here came up with a lightweight solution that addresses this issue by allowing the delegating of security operations. Medical data from IoMT and integrated devices can be collected and stored securely and distributed using a conventional in-depth approach combined with blockchain, making it suitable for healthcare professionals such as nursing homes, hospitals, and the healthcare industry where data exchange is required. The research findings indicate that the suggested system is viable and has a 94.84 $\mathrm{\%}$ security rate, a security performance of 96.4 $\mathrm{\%}$, a success rate of 89.9 $\mathrm{\%}$, and a 5.1 $\mathrm{\%}$ latency rate compared to traditional methods.