Medically Reviewed: Dr Hanif Chatur Introduction Of late, the application of artificial Intelligence has brought about a dramatic revolution within the health industry. This major changes include innovations in most areas of the health informatics and health data management systems. It is AI-enabled tools that have enhanced the areas of note-taking, clinical documentation, transcription from audio to text, thereby providing timely and effective solutions for the medical community. Developments are not scientific and techno-progress, but also become a productive answer for the rising need of effective information management with the growing society. This article explores the historical perspective of artificial intelligence in healthcare data and informatics, highlighting major turning points such as the introduction of #medicalscribe technologies and #clinicaldocumentationimprovement solutions. Furthermore, it examines AI’s role as a #therapyaid, emphasizing its contribution to patient care and hospital system efficiency. Early Beginnings Implementation of AI in healthcare data and informatics has foundations that go back into history within the 1950s and 1960s-the time when artificial intelligence as a term was established. Scholars began to investigate the possibilities of building machines that mimic human thought, which at this point led to some basic rule-based systems and algorithms. Initial pursuits in this area were concerned with extending the use of computers for data processing, data pattern recognition, and medical diagnostic assistance. 1956: The expression “Artificial Intelligence” was first introduced at the Dartmouth meeting, thus establishing the beginning of AI as an area of scientific research. 1960s: Pictures of early incarnations of AI were advanced in creating systems such as MYCIN and DENDRAL, which were made to help with medicine and chemistry respectively, thus giving birth the concept of AI in health care informatics. For example, MYCIN was an expert system built to treat and evaluate the patients suffering from bacterial infections proving how AI can be applied in clinical decision support.   How ML Has Come up in Healthcare Data In fact, the growth of machine learning (ML), which is pure artificial intelligence, is due to the result of varied algorithms that allowed different computers to learn from various data sets over the last decade and a half. This period also signaled the end of rule-based systems, the predominant form of AI before, to more advanced ways of performing AI, especially analysis of complex health care data sets. 1980s: Development of artificial neural networks and the subsequent birth of machine learning gave computers the ability to perform tasks that were strictly human such as pattern detection, which has greatly aided in image based medical diagnosis. 1990s: The advent of electronic health records (EHRs) and a proliferation of clinical databases produced massive amounts of data which could be assimilated by AI systems leading to precise predictive models of disease diagnosis and patient performance. During this time, the use of AI progressed from being limited to assistance in diagnosis to embracing predictive analytics. This saw the development of algorithms which used historical patient data to derive inferences on what health risks and trends could be impending. The Data Explosion and AI’s Role in Informatics To say the least, electronic health records (EHRs), along with even more such things as wearables and so much more, have been amazing and dramatic moves towards almost an electronic format for everything health-related over the past hundred years since the onset of the new millennium: Genomics. Along with this generation of great volumes of data came the need for analysis and management strategies. Therefore, it was The implementation of EHRs started to scale up and brought lots of both primary and secondary information which required processes and means of control. AI made it possible to develop techniques such as data mining and NLP which are very important in making sense of the information contained in clinical documents. From 2010: More advanced processes in such areas as deep learning, natural language processing (NLP), etc. Focusing on medicine developed in such areas as medical imaging and genomic data, expansion of patient monitoring capabilities into more good medicine practice through providing consistent and precise patient-centered care and making better predictions of diseases. These developments set a stage for personalized medicine, where it would be possible for AI to process and interpret several variables, namely genetic profile, habits, and surroundings, and recommend drugs suitable to the person. AI in Clinical Workflows and Decision Support With the evolution of AI technologies, they became easier to incorporate into clinical processes. Designed Decision support systems based on AI started to help health care professionals with their suggestions and carried out some of their day-to-day activities. In 2016, the rise of virtual health assistants, such as IBM’s Watson Health Systems, revealed one of the most challenging capabilities of AI – the ability to mine complex health data and provide clinically useful information to the doctors. 2018: The FDA approved imaging-based AI algorithms for use including the detection of diabetic retinopathy and analyzing cardiac imaging. These approvals were a watershed moment in the history of the adoption of AI in medicine as a reliable diagnostic tool. At this stage, the function of A.I. in healthcare were such that, beside assisting the physicians themselves; it was also optimized workflows further for an even faster and thus more accurate decision making in hospitals and clinics.     The Role of AI During the COVID-19 Pandemic The shift to universal healthcare practices using artificial intelligence became ever more notable during the COVID-19 global health crisis. AI contributed immensely towards coping with this global health challenge, especially in areas that required swift data analysis, contact tracing, or vaccine development. Year 2020: AI algorithms were developed to utilize the existing data of patients and predict the trends for contracting the disease, contributing to better planning of hospitals and helping in the vaccination process. For public health management during the pandemic, contact tracing and predictive modeling were also aided by artificial intelligence algorithms. This era made it clear how AI facilitated large scale collections and management of healthcare information and how it can assist in conducting