Artificial intelligence is revolutionizing — and strengthening — modern healthcare through technologies that can predict, grasp, learn, and act, whether it's employed to identify new relationships between genetic codes or to control surgery-assisting robots. It can detect minor patterns that humans would completely overlook. This study explores and discusses the various modern applications of AI in the health sector. Particularly, the study focuses on three most emerging areas of AI-powered healthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggest that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification. Artificial Intelligence (AI) can help also to eliminate time-consuming data monitoring methods. The findings also indicate that AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results. Medical AI companies develop systems that assist patients at every level. Patients' medical data is also analyzed by clinical intelligence, which provides insights to assist them improve their quality of life.

The healthcare industry has historically been an early adopter of technologyadvancements and has reaped significant benefits. Machine learning (an artificialintelligence subset) is being used in a variety of health-related fields, including theinvention of new medical treatments, the management of patient data and records, andthe treatment of chronic diseases. One of the most important uses of machine learningin healthcare is the detection and diagnosis of diseases and conditions that areotherwise difficult to identify. This can range from tumors that are difficult to detect intheir early stages to other hereditary illnesses. This research identifies and discussesthe various usages of machine learning in medical diagnosis.

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Artificial intelligence is revolutionizing — and strengthening — modern healthcarethrough technologies that can predict, grasp, learn, and act, whether it's employed toidentify new relationships between genetic codes or to control surgery-assisting robots.It can detect minor patterns that humans would completely overlook. This studyexplores and discusses the various modern applications of AI in the health sector.Particularly, the study focuses on three most emerging areas of AI-poweredhealthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggestthat pharmaceutical firms have benefited from AI in healthcare by speeding up theirdrug discovery process and automating target identification. Artificial Intelligence (AI)can help also to eliminate time-consuming data monitoring methods. The findings alsoindicate that AI-assisted clinical trials are capable of handling massive volumes of dataand producing highly accurate results. Medical AI companies develop systems thatassist patients at every level. Patients' medical data is also analyzed by clinicalintelligence, which provides insights to assist them improve their quality of life.

The objective of this research was to investigate benefits and challenges of AI in healthcare. We divided the benefits into two subcategories: benefits related to the medical domain, and the benefits related to economic and social lives domain. The findings are: 1) Smart data inclusion contributes significantly and help to improve decision-making quality. 2) Surgical robots have improved the precision and predictability of the surgery. 3) Intraoperative guidance via video pictures and communication systems has proven to be beneficial, particularly in situations when there is a poor access to clinics, travel limitations, or pandemic. The major challenges are: 1) the data reflects sometimes inherent biases and disparities in the healthcare system. 2) The demand for huge datasets incentivizes developers to acquire data from a large number of patients. Some patients may be worried that this data collection would infringe on their confidentiality. 3) AI systems may occasionally be incorrect, resulting in patient damage or other health-care issues. It is not assumed that a new technology will always be good; it has the potential to be detrimental. There are some improvements that benefit and there are some challenges that may harm, and these challenges must be responded by future research.