Overview of Artificial Intelligence in Health Care System
Dr.Venkata Rama Rao Nallani1, Shree Yasam2, Prof.Rama Rao Nadendla3
Professor &HOD, Dept. of Pharmacy Practice1, V/VI Pharm D 2 , Principal ,Chalapathi Institute of Pharmaceutical Sciences ,Lam,Guntur,AP,India.
Corresponding Author: Dr.Venkata Rama Rao Nallani
MailId: nvramarao009@gmail.com
Artificial Intelligence (AI) is Computers perform tasks that are usually assumed to require human intelligence, is currently being discussed in nearly every domain of science and engineering[1]. Major scientific competitions like ImageNet Large Scale Visual Recognition Challenges are providing evidence that computers can achieve human-like competence in image recognition [1]. All of these developments raise questions regarding how these talents might assist or perhaps improve human decision-making in the areas of health and medical care. At least in very specific cases, two recent high-profile academic articles have shown that AI can conduct clinical diagnoses on medical images at levels equal to experienced physicians[1].
AI in healthcare can be a critical tool for analyzing vast volumes of unique patient and raw medical information to create more accurate diagnoses and treatment plans. It can quickly analyze data from a variety of sources, identify potential problems, and recommend solutions across many contexts, including clinical and administrative environments [1].
The emergence of artificial intelligence (AI) in healthcare has been groundbreaking, reshaping the way we diagnose, treat and monitor patients. This technology is drastically improving healthcare research and outcomes by producing more accurate diagnoses and enabling more personalized treatments[2]. AI in healthcare’s ability to analyze vast amounts of clinical documentation quickly helps medical professionals identify disease markers and trends that would otherwise be overlooked[2]. The potential applications of AI and healthcare are broad and far-reaching, from scanning radiological images for early detection to predicting outcomes from electronic health records[ 2].
