Uses of Machine Learning in Healthcare Sectors

Abstract: When it comes to human health and life, any technology that can provide a more efficient, useful and faster analysis to deliver an appropriate treatment plan on time is of immense value. Artificial intelligence and its division M.L. It is taking over the world now every day little by little business use cases are appearing in technologies news. Industry is same with administration or financial fields seek to be the best choices for technology, but other sectors Are the healthcare industry any different or same? Be sure not. Speaking of AI in the field of medicine, we must realize the huge potential and changes that M.L can contribute to the healthcare industry.

M.L. for healthcare technologies provides algorithms with self-learning neural networks that can increase the quality of treatment by analyzing external data on a patient’s condition, their X-rays, CT scans, various tests, and screenings. Also, worth mentioning, deep learning is now primarily used for detecting cancer cells.

In this paper we attempt to explain how ML can transform management and both patient care methods in the technology and industry. We have papers and researches to prove M.L. often outperforms humans at diagnosing the disease. Algorithms are doing a good and high jump of detecting malignancies compare with actual radiologists. M.L. for medical technology companies offers algorithms with convolution learning neural networks or other types of algorithms that can increase the quality of treatment by analyzing external data about a patient’s status, it is noteworthy that deep learning is now mainly used to detect cancer cells. Also CT scans, x-rays, and various medical tests and examinations or screenings.

However, there is high distance from complete replace humans by smart devices or robotic in all Medicine fields. This research is focused about advance uses of A.I. in medical and healthcare sectors and the drawbacks or limitations for broader uses.

Keywords: Artificial Intelligence (A.I.), Machine learning (ML), Healthcare, Diagnosis and disease identification, diagnoses via image analysis

DOI:https://doi.org/10.17762/msea.v71i4.1099