Leveraging Artificial Intelligence in Patient Care

Abstract

Artificial intelligence (AI) is a prominent tool that enables people to rethink how they consolidate information, analyze data, and use the observations to improve decision making, and it is already revolutionizing every walk of life. The objective of AI is to model human intellectual functions. It is causing a fundamental change in healthcare, thanks to the growing availability of healthcare data and the rapid advancement of analytics techniques. The healthcare market for AI is rapidly increasing at a rate of 40%, and by the end of 2021, it is expected to reach $6.6 billion. Deep neural networks, natural language processing, computer vision, and robotics have all made significant advances in artificial intelligence (AI) in recent years. These techniques are already being used in healthcare, with AI anticipated to take over many of the tasks currently performed by clinicians and administrators in the future. Patient administration, clinical decision support, patient monitoring, and healthcare treatments are the four primary areas where AI will have the largest impact. Many elements of patient care, as well as administrative operations inside providers, payers, and pharmaceutical companies, could be 58transformed by these technologies. The approach to medicine is progressing with the advancement of new (AI) methods of machine learning. Conjoined with rapid improvements in computer processing, these AI-based systems are already enhancing the accuracy and efficiency of diagnosis and treatment across various specializations. The developing focus of AI in radiology has led some experts to suggest that someday AI may even substitute radiologists. A number of studies have already shown that AI can perform as well as or better than humans at crucial healthcare activities like disease diagnosis. Algorithms are already surpassing radiologists in terms of detecting dangerous tumors and advising researchers on how to build cohorts for expensive clinical trials. However, we believe it will be several years before AI replaces humans in large medical process domains for a variety of reasons. Unquestionably, AI is the most considered issue today in medical imaging research, both in diagnostic and therapeutic areas. Scientists have enforced AI to automatically analyze complex patterns in imaging data and help in quantitative assessments of radiographic characteristics. In radiation oncology, AI has been applied to different image procedures that are used at different stages of the treatment, i.e., tumor declination and treatment assessment. For example, AI is essential for boosting power for processing a huge number of medical images and therefore brings to light disease characteristics that are not seen by the naked eye. The utilization of AI within the diagnostic process aiding medical specialists could be of great potential for the healthcare sector and the overall patient’s well-being. The assimilation of AI into the current technical framework stimulates the identification of relevant medical data from multiple sources, which is tailored to the needs of the patient and the treatment process. Simultaneously, AI unchains silo thinking, such as sharing knowledge across departmental boundaries, as information from all involved areas is taken into account. Furthermore, AI develops results based on a larger community rather than on subjective experiences and achieves equal outcomes when using similar medical data and does not depend on situations, emotions, or time of day.

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