Electronic health records (EHRs) are digitally saved health records that provide information about a person’s health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of EHR management and create a more secure, decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. When smart contracts are used to make decisions and conduct analytics with machine-learning algorithms, the results may be trusted and unquestioned. However, Blockchain is not always indestructible and suffers from scalability and complexity issues that might render it inefficient. Combining AI and blockchain technology can handled some of the drawbacks of these two technical ecosystems effectively. AI algorithms rely on data or information to learn, analyze, and reach conclusions. The performance of AI algorithms is enhanced through the data obtained from a data repository or a reliable, secure, trustworthy, and credible platform. Researchers have identified three categories of blockchain-based potential solutions for the management of electronic health records: conceptual, prototype, and implemented. The purpose of this research work is to conduct a Systematic Literature Review (SLR) to identify and assess research articles that were either conceptual or implemented to manage EHRs using blockchain technology. The study conducts a comprehensive evaluation of the literature on blockchain technology and enhanced health record management systems utilizing artificial intelligence technologies. The study examined 189 research papers collected from various publication categories. The in-depth analysis focuses on the privacy, security, accessibility, and scalability of publications. The SLR has illustrated that blockchain technology has the potential to deliver decentralization, security, and privacy that are frequently lacking in traditional EHRs. Additionally, the outcomes of the extensive analysis inform future researchers about the type of blockchain to use in their research. Additionally, methods used in healthcare are summarized per application area while their pros and cons are highlighted. Finally, the emphasized taxonomy combines blockchain and artificial intelligence, which enables us to analyze possible blockchain and artificial intelligence applications in health records management systems. The article ends with a discussion on open issues for research and future directions.

Accessing healthcare services by several stakeholders for diagnosis and treatment has become quite prevalent owing to the improvement in the industry and high levels of patient mobility. Due to the confidentiality and high sensitivity of electronic healthcare records (EHR), the majority of EHR data sharing is still conducted via fax or mail because of the lack of systematic infrastructure support for secure and reliable health data transfer, delaying the process of patient care. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. The objective of this research is to develop a Patient-Centered Blockchain-Based EHR Management (PCEHRM) system that allows patients to manage their healthcare records across multiple stakeholders and to facilitate patient privacy and control without the need for a centralized infrastructure by means of granting or revoking access or viewing one’s records. We used an Ethereum blockchain and IPFS (inter-planetary file system) to store records because of its advantage of being distributed and ensuring the immutability of records and allowing for the decentralized storage of medical metadata, such as medical reports. To achieve secure a distributed, and trustworthy access control policy, we proposed an Ethereum smart contract termed the patient-centric access control protocol. We demonstrate how the PCEHRM system design enables stakeholders such as patients, labs, researchers, etc., to obtain patient-centric data in a distributed and secure manner and integrate utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions. Finally, we tested the proposed framework in the Windows environment by compiling a smart contract prototype using Truffle and deploy on Ethereum using Web3. The proposed system was evaluated in terms of the projected medical data storage costs for the IPFS on blockchain, and the execution time for a different number of peers and document sizes. The findings of the study indicate that the proposed strategy is both efficient and practicable.

Skin disease is the most common disease in the world. The diagnosis of the skin disease requires a high level of expertise and accuracy for dermatologist, so computer aided skin disease diagnosis model is proposed to provide more objective and reliable solution. Many researches were done to help detect skin diseases like skin cancer and tumor skin. But the accurate recognition of the disease is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between Disease and non-Disease area, etc. This paper aims to detect skin disease from the skin image and to analyze this image by applying filter to remove noise or unwanted things, convert the image to grey to help in the processing and get the useful information. This help to give evidence for any type of skin disease and illustrate emergency orientation. Analysis result of this study can support doctor to help in initial diagnoses and to know the type of disease. That is compatible with skin and to avoid side effects.

To secure sensitive medical records in the healthcare clouds, this paper proposes an End-to-End Encryption (E2EE) to enhance a patient-centric blockchain-based system for electronic health record (EHR) management. The suggested system with a focus on the patient enables individuals to oversee their medical records within various involved parties by authorizing or withdrawing permission for access to their records. Utilizing the inter-planetary file system (IPFS) for record storage is chosen due to its decentralized nature and its ability to guarantee the unchangeability of records. Then an E2EE enhancement maintains the medical data integrity using dual level-Hybrid encryption: symmetric Advanced Encryption Standard (AES) and asymmetric Elliptic Curve Cryptography (ECC) cryptographic techniques. The proposed system is implemented using the Ethereum blockchain system for EHR data sharing and integration utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions over the IPFS cloud. The proposed system performance is evaluated in a working system prototype. For different file sizes between 512 KB to 100 MB, the performance metrics used to evaluate the proposed system were the time consumed for generating key, encryption, and decryption. The results demonstrate the proposed system’s superiority over other cutting-edge systems and its practical ability to share secure health data in cloud environments.

Nowadays, with large quantities of data in every industry and the advancement of technology, solutions to a wide range of problems can be resolved. In this paper, Machine Learning and Blockchain are used to propose a solution to difficulties linked to healthcare data management systems. Machine Learning allows the extraction of needed-only information that is relevant from data. This is achieved using trained algorithms that provide an intelligent decision strategy based on Convolutional Neural Networks (CNN) to automatically extract high-level semantic information from electronic medical records and then perform automatic diagnosis. Once medical data is saved, the next issue is data sharing and reliability. This is where Blockchain technology comes into play. The Blockchain with consensus protocol ensures that information is authentic, and transactions are safe. By putting the patient at the center of the healthcare system and boosting the privacy and interoperability of health data, this proposed solution can improve health care administration. This paper focuses on using Blockchain technology to solve healthcare data management problems while also incorporating some essential Machine Learning features. The expected result of the proposed system 98.67% accuracy and 96.02% recall, demonstrating that employing a convolutional neural network to learn high-level semantic aspects of electronic medical records and then undertake assist diagnosis is feasible and valuable.

Health care mobile application delivers the right information at the right time and place to benefit patient’s clinicians and managers to make correct and accurate decisions in health care fields, safer care and less waste, errors, delays and duplicated errors.Lots of people have knowledge a skin illness at some point of their life, For the reason that skin is the body's major organ and it is quite exposed, significantly increasing its hazard of starting to be diseased or ruined.This paper aims to detect skin disease by mobile app using android platform providing valid trustworthy and useful dermatological information on over 4 skin diseases such as acne, psoriasis content for each skin condition, skin rush and Melanoma. It will include name, image, description, symptoms, treatment and prevention with support multi languages English and Bahasa and Mandarin. the application has the ability to take and send video as well as normal and magnified photos to your dermatologist as an email attachment with comments on safe secure network, this app also has a built in protected privacy features to access to your photo and video dermatologists. The mobile application help in diagnose and treat their patients without an office visit teledermatology is recognized by all major insurance companies doctor.