Artificial Intelligence in Medical refers to the use of artificial intelligence automated processes in the diagnosis and treatment of the patient who cares. The purpose is to make computers more useful in solving problematic healthcare challenges and by using computers we can interpret data which is obtained by the diagnosis of various chronic diseases. AI in medicine can be dichotomized into two subtypes: Virtual and Physical. The virtual part ranges from applications such as electronic health record systems to neural network-based guidance in treatment decisions. The physical part deals with robots assisting in performing surgeries, intelligent prostheses for Handicapped people, and elderly care Most applications of AI in medicine read in some type of data, either numerical (such as heart rate or blood pressure) or image-based (such as MRI scans or Images of Biopsy Tissue Samples) as an input.

The algorithms then learn from the data and churn out either a probability or a classification the algorithm learns by example. They trained the system by using more than 42,000 patient CT scans (both current and prior scans) to predict lung cancer risk. These CT scans were from patients whose diagnoses were known; some already had lung cancer, some did not, and some had lumps that later turned cancerous. The algorithm was tested against 6716 cases with known diagnoses and was found to be 94% accurate.

In the present-day AI usage in medical Science not only reduces manual labour and frees up the primary care physician's time but also increases productivity, precision, and efficacy. Searching and developing pharmaceutical agents against a specific disease via clinical trials take years and cost a gazillion dollars. It can increase productivity and care delivery efficiency and allow healthcare systems to provide more and better care to more people. AI can help improve healthcare practitioners' experience, enabling them to spend more time in direct patient care and reducing burnout.

Ram Bohra

7th Sem CSE