Adoption of Blockchain-based Artificial Intelligence in Healthcare Mir Hassan Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan, China hassanmir@hust.edu.cn Chuanbo Zhu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan, China chuanbo_zhu@hust.edu.cn Jincai Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan, China jcchen@hust.edu.cn Umer Zukaib Department of Computer Science COMSATS University Islamabad Abbottabad Campus, Pakistan umerzukaib@cuiatd.edu.pk Abstract—Data and technology have made it possible to find solutions to a wide variety of challenges in healthcare. Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. It has been demonstrated that machine learning may be useful in a variety of fields because of its ability to recognize patterns in data, perform analyses, and reach choices. To create appropriate choices, machine learning requires a sufficient amount of data. Data sharing and data reliability are critical components of machine learning in order to increase its accuracy. Blockchain Technology's decentralized database places a premium on data exchange. Consensus in Blockchain technology ensures the legitimacy and security of data. Converging these two technologies can result in highly accurate machine learning results combined with the security and stability of Blockchain Technology. In this paper, we will have opportunity to know about Machine learning integration along with Blockchain in the field of Healthcare. We proposed secure, transparent and intelligent methods in the Smart Health care Industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system. Keywords—Blockchain technology, machine learning, smart system, healthcare I. INTRODUCTION Frost \& Sullivan expects that during the next five to ten years, healthcare companies will have access to integrated health IT platforms based on an emerging collection of information management technologies, such as blockchain [1], the Internet of Things (IoT) [2], and machine learning [3]. When it comes to storing and exchanging health data, privacy is a key problem, and with current healthcare data storage systems lacking top-tier security, blockchain could give a solution to vulnerabilities like hacking and data theft. Interoperability is a feature of blockchain technology in healthcare that allows for the secure interchange of medical data among the various systems and employees involved, resulting in a number of benefits such as improved communication, time savings, and operational efficiency. According to the report, due to difficulties such as errors, duplications, and inaccurate billing, the use of blockchain technology for claims adjudication and billing management applications is expected to expand by 66.5 percent by 2025. With blockchain, all of these issues can be solved. Machine learning is a computer-based method for analyzing free-form text or voice using a preset set of theories and technologies, such as linguistic and statistical methodologies, to extract rules and patterns from the data. Knowledge and experience are two very significant variables for physicians in terms of patient care; yet, humans are limited in terms of gaining knowledge through accumulating data, whereas machine learning excels in this area [4]. In this manuscript, we introduce the Blockchain technology integrated with Machine learning techniques together to enable the improvements of the security level of patient’s data and patient’s diagnosis. It also helps the researchers, doctors, and medical practitioners to evaluate the treatment of patients through a smart healthcare system. II. LITERATURE REVIEW Due to heightened danger of pandemic, the World Health Organization (WHO) has suggested that countries develop a "Pandemic Plan." A Pandemic Plan is often designed in accordance with the WHO's pandemic phases, with the goal of achieving unambiguous results in pandemic management from the start [4]. Different techniques to preparing for an emergency may be recognized in healthcare; in fact, each disaster is divided into four phases: mitigation, preparation, reaction, and recuperation [5]. The "tabletop exercise" is a valuable technique for simulating the creation of a crisis situation; it creates a scenario that benefits from both communication and cooperation among various sectors and areas, such as management, workers, logistics, communication, and finance. A proper method could give a broad framework as well as a mental model that replicates the ideal environment for future decision-making [5]. 2022 IEEE The 5th International Conference on Artificial Intelligence and Big Data 140 978-1-6654-9913-2/22/$31.00 ©2022 IEEE 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD) | 978-1-6654-9913-2/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICAIBD55127.2022.9820137 Authorized licensed use limited to: UNIVERSITA TRENTO. 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