Current issue

        Artificial Intelligence is misleading us in a number of ways as it suggests that there should be more advancement and development in the technology in comparison to the current situation.  Presently, in the various areas and fields of life machines having learning methods are just capable to access the artificial narrow intelligence.  However, development in this field is still in under-process at uncreditable speed. The human being is even defeated by the programs that have narrow intelligence (Poole & Mackworth, 2017).  For instance, the super computer of IBM named Deep Blue winning the game of chess but unlike the champions of the human world. While on the hand such computers are not capable to drive a car efficiently or draw new arts. Computers have the capability to understand the videos and images. The computer also understands the text from different natural language processing frames. such functions of the computer are assisting the medical and health care fields (e.g. medical imaging).

        A researcher Michelle Zhou, who was working in the I.B.M Watson group and IBM research (before his decision of leaving to start up “Juii” as co-founder) concluded his research studies by categorizing the artificial narrow intelligence (ANI) as the initial or first stage of the artificial intelligence (AI). In accordance with his concept towards recognition intelligence, the computers in the healthcare centers are capable to recognize patterns. artificial intelligence (AI) also enable the computers to collect topics from the related test blocks or understanding the possible meaning of complete documents just from the few sentences of the document. artificial general intelligence (AGI) support the machines to get understanding towards the abstract concepts of the objects with just having the little experience and transfer the collected information and abstract concepts between the relevant domains (Jones, 2015).   

Specific problem(s) underpinning the issue of Artificial Intelligence in Medical Sector

            In order to eradicate the problem of over-hyping the technology, limitations concerning with artificial narrow intelligence (ANI) need to be taken into knowledge. while for radiology ( in the case using machines for learning, learning algorithms, and image recognition) there are some risks factors such as risk related to feeding the machine (e.g. computer system) with images in thousands and underlying bias.

            The abilities of the smart algorithm for right predictions and forecasting can be proved as useless when it comes to the novel cases. For instance, algorithm and conceptualizing the framework capabilities are unable to understand the subjective assumptions drug side effects and drugs treatment resistance (Agah, 2013).    

.      narrow intelligence in the administration. As algorithms work when the presented records are organized as an understandable and sensible sequence.  Still in a number of hospitals doctors are using patient’s reports and files for scribbling notes (Fieschi, 2013). Though research it is also proved that Artificial intelligence has a significant role in the decision making process  (Aizenberga, Aizenberga, Hiltner, Moraga, & Bexten, 2001).  

             In accordance with a research results (based on the survey of 100 organization) almost 77% of the total 100 were expected to have future investment in IoT, %53 in Artificial intelligence and more than 81% of executives were supporting the opinion that the organization in which they were working cannot adopt the Artificial intelligence because of the fear of stakeholders. As there are chances for the liability and societal issues about the right justification for the decisions taken according to the results and predictions of the artificial intelligence (Hengstler & Ellen Enkel, 2016).  Therefore, 73% of the organizations are going to develop ethical standards for artificial intelligence with the vision to make it clear that artificial intelligence in their systems is capable to work transparently and responsibly.   

                While there are chances of manipulation and inaccuracy of the data used for results thus wrong data will present skewed and corrupted results (Aizenberga, Aizenberga, Hiltner, Moraga, & Bexten, 2001). In addition to this, another important and critical point presented by the research is that organization (86%) were not using the appropriate measures for data verification and checking data accuracy to generate the accurate automated decisions from the data.

            While artificial intelligence is working continuously to bring improvement and collaborate with other conquering entities in order to develop meaningful and desired relationships with the patients of the healthcare centers. In accordance with the research, study organizations should ensure accuracy, transparency and zero manipulation of the data before working on the suggested results (Setiawan, P.A.Venkatachalam, & M.Hani, 2009).   

Proposed solution – NOVELTY of Artificial Intelligence in Medical Sector

                Machine learning and Artificial Intelligence (AI) both are Dependent on datasets undoubtedly. Without datasets, AI cannot work effectively.  Therefore structured and raw data sets in the healthcare and medical sector can be collected from the HITECH. However when it comes to the combine and integrate the knowledge catered from different genres of datasets Artificial Intelligence (AI) face challenges. (Hengstler & Ellen Enkel, 2016) While there is also a need to consider the financial budget as the dataset management require a huge amount of budget. Artificial Intelligence (AI) cannot work independently without getting support from the human being. Real time data sharing is also an issue for Artificial Intelligence (AI). In addition to this, there is also the need to share data related to case studies and research to continuously up-dating the Artificial Intelligence (AI) system.

        There is need to introduce interoperability in the healthcare centers of the same regions, in order to make the data available about the patients, and payers. For this purpose complex machines systems are required that can share information about patient and provided treatment with interoperate of region (Hengstler & Ellen Enkel, 2016).     

        In the healthcare center we can provide bridge through the augmented reality technology to support the people, information and immersive experience in reducing gaps.

        ECOSYSTEMS CAN BE PROMOTED THROUGH TECHNOLOGY BASED PARTNERSHIP. BLOCK CHAIN AND MICROSERVICE ARE PLAYING THEIR ROLES IN THIS WAY. WHILE IN HEALTH CARE CENTERS BLOCK CHAIN IS CONSIDERED CRITICAL FOR THE ORGANIZATION.

       Self-maintaining equipment and automatically managing fluids in the healthcare center are the examples of the robotic, artificial intelligence and connected devices that are ENSURING THE SERVICES OF THE HEALTH CARE CENTER INEFFECTIVE WAY.     

        The new technology can be developed in hospitals, particularly in the icu ROOMS AND THIS TECHNOLOGY CAN MANAGE THE FLUID LEVELS IN THE PATIENT AUTOMATICALLY BY AN EQUIPMENT OF SELF-MAINTAINING. THE INTELLIGENT ENVIRONMENT CAN BE DESIGNED IN THE HEALTH ORGANIZATIONS BY USING ROBOTICS, CONNECTED DEVICES, AND THE AL. BUT IN THE REAL CASE THE TECHNICAL INFRASTRUCTURE IS NOT DESIGNED THAT PROVides supports to the hyper connected environment. The major part of health executives as four-fifth at 82% works on the basis of edge architecture and requires maturity of new techniques (Jiang, et al., 2017). AT THE SAME TIME 85% BELIEVES ON THE INSIGHTS OF REAL TIME FOR THE VOLUME RELATED TO THE DATA AS EXPECTED. IN THE NEAR FUTURE THE COMPUTING PROCESS THAT IS AT THE MAXIMUM FOR THE GENERATION OF THE DATA. BUT STILL IN THE HEALTH EXECUTIVES (86%), THE BALANCE COMPUTING PROCESS ARE MAXIMIZED FOR THE INFRASTRUCTURE OF TECHNOLOGY. THE MAXIMUM CONSIDERATION IS RELATED TO THE CLOUD AND THE COMPUTING PROCESS IS CONSIDERED AS ESSENTIAL PART FOR THE DEVELOPMENT OF HEALTH RESEARCHES AND RESOURCES. THE MAXIMUM TECHNOLOGY INFRASTRUCTURE ENABLES THE INTELLIGENCE TO WORK EFFICIENTLY (Aizenberga, Aizenberga, Hiltner, Moraga, & Bexten, 2001).         

References of PROBLEM STATEMENT OF ARTIFICIAL INTELLIGENCE IN MEDICAL SECTOR

Agah, Arvin. 2013. Medical Applications of Artificial Intelligence. CRC Press. Accessed 11 13, 2018.

Aizenberga, Aizenberga, J. Hiltner, C. Moraga, and E. Meyer zu Bexten. 2001. "Cellular neural networks and computational intelligence in medical image processing." Image and Vision Computing 177–183.

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

Fieschi, M. 2013. Artificial Intelligence in Medicine: Expert Systems. Springer. Accessed 11 13, 2018.

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

Hengstler, Monika, and Selina Duelli Ellen Enkel. 2016. "Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices." Technological Forecasting & Social Change 105: 105-120.

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.

Jones, M. Tim. 2015. Artificial Intelligence: A Systems Approach: A Systems Approach. Jones & Bartlett Learning. 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.

Poole, David L., and Alan K. Mackworth. 2017. Artificial Intelligence. Cambridge University Press. Accessed 11 13, 2018.

Setiawan, Noor Akhmad, P.A.Venkatachalam, and Ahmad Fadzil M.Hani. 2009. "Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System." Proceedings of the International Conference on Man-Machine Systems. Accessed 11 13, 2018.