The complexity and increase in data in healthcare mean that artificial intelligence (AI) will be increasingly applied in this field. Payers and care providers and life science companies are already employing several types of AI. While there are many cases where AI can perform healthcare tasks as well or better than humans, implementation factors will prevent the large-scale automation of the jobs of health professionals for a significant period of time. AI can be applied to several types of health data (structured and unstructured).
The most popular AI techniques include machine learning methods for structured data, such as the classic vector support machine and the neural network, and modern deep learning, as well as natural language processing for unstructured data. The main areas of disease that use AI tools include cancer, neurology and cardiology. Ethical issues in the application of AI to healthcare are also discussed. We analyze the current state of AI applications in healthcare and analyze their future.
We review in more detail the applications of AI in stroke, in the three main areas of early detection and diagnosis, treatment, as well as the prediction of outcomes and the evaluation of the prognosis. Mobile health (m-health) is the term for monitoring health using mobile phones and patient monitoring devices, etc. It has often been regarded as the substantial advance in technology in this modern era. Recently, artificial intelligence (AI) and big data analysis have been applied to mobile health to provide an effective health system.
Modern medical research has used various types of data, such as electronic medical records (EHR), medical images and complicated texts, which are diversified, misinterpreted and highly disorganized. This is a major reason for the appearance of several disorganized and unstructured data sets due to the emergence of mobile applications along with health systems. In this article, a systematic review is carried out on the application of AI and the analysis of big data to improve the mobile health system. A number of AI-based big data algorithms and frameworks are also discussed with respect to the data source, the techniques used, and the area of application. This article explores the applications of AI and big data analysis to provide information to users and allow them to plan, using resources especially for the specific challenges of mobile health, and proposes a model based on AI and big data analysis for mobile health. The findings in this article will guide the development of techniques that use the combination of AI and big data as a source to manage mobile health data more effectively.
Although this work has not been done, thanks to the modularity of the embedding extraction process in the HAIM framework, other models or pre-trained systems could be added to generate embeddings from other types of data sources if necessary (EOther (n, t)). The main objective of designing the mobile health-based system is to help users in one or more health domains. Section 5 presents the applications of big data analysis in mobile health, followed by an additional summary of its applications in the health sector. Similarly, the application of AI and the analysis of big data in healthcare are considered to be one of the important achievements of the intelligent health system. EHRs, EMRs, PHR, software for managing doctors' offices, and several other components of healthcare data increase service quality and efficiency and reduce the total cost of health care and medical errors. It is important that health institutions, as well as government and regulatory bodies, establish structures to monitor key issues, react responsibly, and establish governance mechanisms to limit negative implications.
They are slowly being replaced in healthcare by more data-based approaches and machine learning algorithms. The increasing availability of health data and the rapid development of big data analysis methods have made possible the recent successful applications of AI in healthcare. Artificial intelligence (AI) is rapidly entering healthcare and plays important roles, from automating routine tasks in medical practice to managing patients and medical resources. The main advantages of these EHRs are that they allow faster data retrieval and that health professionals have better access to all patient medical records and medical data. However, in a survey of 500 US users of the five most used chatbots in healthcare, patients expressed concern about disclosing confidential information, talking about complex health issues and their poor usability. A more complex form of machine learning is the neural network, a technology that has been available since the 1960s, has been well established in healthcare research for several decades (3) and has been used for categorization applications such as determining if a patient will contract a particular disease. Sengupta “Artificial intelligence in cardiovascular medicine” vol. Artificial intelligence (AI) and machine learning (ML) systems are about to become fundamental tools in clinical practice and health care operations1.
