In the medical health care field, artificial intelligence is a new butan innovative concept that is growing rapidly in the sector. However in comparison to the other fields and areas of life medical and health care sectors are still in the condition of infancy towards the adoption of artificial intelligence in their sectors. A number of reasons are contributing to control the rapid adoption of artificial intelligence in the medical fields. Providers are updating their processes and tools in this current age to provide the basis for the right application and adoption of artificial intelligence. However analysis conducted to predict the future of artificial intelligence in the health care center presents that in 2021 health market growth will reach the $6.6 billion dollar target and at the same times the key clinical health care centers will create the estimated amount of 150 billion dollars (annual saving) until 2026 through the application of the artificial intelligence in the health care center (Jiang, et al. 2017).  

        Artificial intelligence has a number of benefits for the healthcare centers as artificial intelligence is not only concerned with the improvement for the bottom line but also the health of the patients. Through the use of tools and insights offered by the artificial intelligence health care centers can bring improvement in their services offered to the patients. However other than all these benefits, there are some challenges also. Application and adoption of the artificial intelligence in the health care centers or the medical field without having the understanding of the challenges can cause to have the impact on the overall industry (Koh and Tan 2011).    

        Other than all, one more important concern for the health care centers is related to the data collection for artificial intelligence. Basically, artificial intelligence efficiency directly depends upon the collected data and its accuracy. Therefore for the right implementation of artificial intelligence in the health care centers enough required data should be there that computer can use to understand the situation. In the artificial intelligence provider of the AI rely on the trust as the computer and machines having AI are built on deep learning and such machines and computers learn through the use of examples and data built in the memory, but there is no significant way to measure and determine the inner working (Harris 2010). However, it is assumed that machines and computers having the artificial intelligence can operate in a better and faster way as compared to the human being.           

        In accordance with the research, deep learning facilitates the artificial intelligence (AI) to diagnose the illness and diseases in the patients. A report on breast cancer in the woman of United states elaborate that one in eight women (living in the United States) has the chances to become patient of breast cancer during her lifetime. While the problem can be controlled in two-thirds of the affected woman if detected at the early stage. Medication at an early stage can save the life of that woman from the invasive breast cancer. For this purpose modern technology as Digital Breast Tom Synthesis are used that provide the solution through screening and diagnostic mammography.     

        In the modern world, artificial intelligence (AI)  can also provide leverage to the radiologist through the inbuilt deep learning tools that support in reducing their interpretation time for DBT and bring improvement in reading workflow, as this solution has the capability to highlight the concerning areas automatically (IntroBooks 2018).  

        Adoption of the artificial intelligence (AI) is causing to bring changes in the ways how the healthcare providers and radiologist perform their duties. In order to bring improvement though enabling the healthcare centers to overcome on the concerns and fears directly associated with the artificial intelligence (AI), there is need to find the right solution in advance through analyzing and researching, prior to taking the decision of implementation.  After that, it is the duty of the provider to spend time in understanding that how the system is working to capture and collect data with the purpose of analysis and checking the errors. In the present age, the whole health care industry is continuously working for the value-based care model. Therefore we can say that health care centers having artificial intelligence (AI) with full understanding towards utilizing its unique and innovative capabilities will surely create differentiation and competitive advantage (Boden 1996).        

References of BRIEF OVERVIEW OF THE STUDY OF ARTIFICIAL INTELLIGENCE IN MEDICAL SECTOR

Boden, Margaret A. 1996. Artificial Intelligence. Elsevier. Accessed 11 13, 2018.

Harris, Michael C. 2010. Artificial Intelligence. Marshall Cavendish. Accessed 11 13, 2018.

IntroBooks. 2018. Artificial Intelligence. IntroBooks. Accessed 11 13, 2018.

Jiang, Fei, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang. 2017. "Artificial intelligence in healthcare: past, present and future." Stroke and vascular neurology 2 (4): 230-243. Accessed 11 13, 2018.

Koh, Hian Chye, and Gerald Tan. 2011. "Data Mining Applications in Healthcare." Journal of Healthcare Information Management 19 (2): 65. Accessed 11 13, 2018.