Adoption of artificial intelligence in healthcare

Discuss how Artificial Intelligence is Affecting Healthcare.

Recently artificial intelligence has sent enormous effect across the healthcare. It is assessed that active evaluation of artificial intelligence doctors will ultimately replace the human physicians in the future. This literature depicts that human physicians will not be replaced by the machines in the predictable future. However, artificial intelligence can definitely help the physicians in order to make an effective clinical judgment or even replace the human decision in some functional areas of healthcare. The gaining availability of healthcare information and rapid expansion of big data analytic technique has made possible the current successful application of artificial intelligence in healthcare. The powerful artificial intelligence can release the hidden clinically data from the huge amount of facts and figures (Jiang et. al., 2017). As a result, it aids to make clinical decision judgment.   

Research Problem	Sub-problem	Collected Literature Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper
Artificial intelligence in healthcare: past, present, and future	· current status of AI applications in healthcare · Adoption of artificial intelligence in healthcare	(Jiang, et. al., 2017).
Role of Artificial Intelligence in Health Care	· detection of various chronic diseases · Various automated systems and tools to minimize errors and control disease progression	(Mishra, et. al., 2017).
Big Data, Analytics & Artificial Intelligence: The Future of Health Care is Here	· access the information to care for patients in their home and their office · Adoption of artificial intelligence in healthcare	(UCSF, 2018).

Compare and contrast the features of qualitative and quantitative methodologies

	Qualitative research	Quantitative research
The objective of the investigation	To increase the depth qualitative knowledge about the basic concept of how artificial intelligence is affecting Healthcare (Wong, and Bressler, 2016).	To obtain the factual information about research concern by considering the views of research candidates towards the research concern (Baig, et. al., 2017).
Sample size	In this, a small number of research candidates are selected.	Lage number of participants will be selected to perform the study.
The technique of Data gathering	Unstructured data collection technique	Structured data collection technique
Nature of data analysis	Non-statistical data analysis method	Statistical  data analysis method

The mixed research design methodology would be applied to this research project as it contains the features of both qualitative and quantitative research design. In this project, qualitative research design will be applied to build theoretical aspect regarding how artificial intelligence is affecting Healthcare. Apart from this, quantitative research design will be practiced for assessing the numeric information with respect to research issue (Ashrafian, Darzi, and Athanasiou, 2015).

Selection of subproblem

There are certain issues regarding how artificial intelligence is affecting Healthcare. But, here, I would like to discuss the concern of Adoption of artificial intelligence in health care. It is not sufficient to identify the current status of AI applications in healthcare but also needs to access the information to care for patients in their home and their office (Park, and Shin, 2017).

Analysis of the selected Methodologies

Literature1. Artificial intelligence in healthcare: past, present, and future

The key purpose of this literature is to identify the Artificial intelligence in order to take off the human cognitive functions. This literature evaluates the paradigm that shifts to healthcare, the rapid expansion of analytics techniques as well as increasing existence of healthcare data. This research surveys the existing status of AI applications in the healthcare and also evaluates its future. Artificial intelligence could be implemented to different kinds of health care facts and figures i.e. unstructured and structured (Capone, et. al., 2016).

Popular artificial intelligence techniques involve the machine learning technique for structured data like classical techniques aid the vector machine and neural network, and the modern deep learning and natural language transformation for unstructured information. Major disease areas that could be solved through artificial intelligence are neurology, cardiology, and cancer. It also reviews the information of artificial intelligence applications in depth.   There are three major areas of early detection, treatment, diagnosis, prognosis evaluation and outcome estimation. It also defines the pioneer artificial intelligence system like IBM Watson as well as hurdles for real-life deployment of artificial intelligence (Wang, et. al., 2017).

Detection of chronic diseases

Literature2.  Role of Artificial Intelligence in Health Care

The key aim of artificial intelligence is to develop the computers more useful in solving the challenges of problematic healthcare and using the computers to interpret the data. It is obtained through the diagnosis of different chronic diseases such as Diabetes, Alzheimer, various types of cancers like colon cancer and breast cancer, and cardiovascular diseases. It also aids in early detection of several chronic diseases that decline the economic burden and severity of the disease. Several automated systems and techniques are used to avoid the errors and control the progression of the disease. These techniques are ASL-MRI, Natural language processing (NLP), Brain-computer interfaces (BCIs), arterial spin labeling (ASL) imaging, biomarkers, and different algorithms (Lu, et. al., 2018).

The computer-assisted in decision support system, diagnosis, the expert system as well as the implementation of software may aid the physicians in order to eliminate the inter-observer variability. The fuzzy approach could be applied to deal with a diverse set of medical information and rationalizing the procedure of diagnosis artificial intelligence techniques particularly, artificial neural networks (ANN). This literature also evaluates that ANN tool addresses the hidden patterns and association in medical data. It is beneficial to design the support system in the field of the clinic. The use of AI provides a discussion of outcome with high accuracy with speed (Schnurr, et. al., 2018). 

Machine learning has reformed the way of life from recognition of language, handwriting, weather forecasting, recognition of handwriting, GPS route to online search engines and image retrieval. This time focuses on successes in machine learning for biology discovery in order to get the benefit for human health. Artificial intelligence is a division of computer science that has potential to assess the complex facts and figures (Russell, et. al., 2015).

Furthermore, the potential is to take out the meaningful association with a set of data. It could be practiced in the treatment; diagnosis and predicting results in many clinical situations. The purpose of artificial intelligence is to make computers more beneficial and comprehend the principles. Non-communicable diseases are an alias as a chronic disease that could not be passed from person to person. They are of long duration with slow progression. This literature review depicts the four main kinds of non-communicable disease such as cardiovascular diseases, chronic respiratory diseases, cancers, and diabetes (Beam, and Kohane, 2016).).

Literature3. Big Data, Analytics & Artificial Intelligence: the Future of Health Care is Here

Automated tools for diagnosis and treatment

This literature review focuses on the Big Data, Analytics & Artificial Intelligence in health care. The healthcare industry is a universe to implement the artificial intelligence. There are certain industries, which are comprehensive, complex, and expensive as a medicine. It creates slowness to maintain digital technology and assess the power of data in order to enhance the results. This literature review focuses on the transformative power of digital in health care. It also facilitates the overview of future digital health. It discusses the skepticism and hype and what is required for the medical community in order to embrace the world in which data, analytics, and machines are employed in order to deliver the higher quality as well as more efficient care. It also demonstrates the real cases regarding financial and clinical benefits to incorporate the digital technology into the care of patient and workflow (Peek, et. al., 2015).

In modern times, digital health shows the advanced analytics based as per the multi-model information. The healthcare internet of things uses apps, sensor, and remote monitoring to offer the continuous clinical data and cloud-based data. Information system enables the clinicians to access the data they need to care for patients in their office, homes or 30 miles away in order to collaborate with a specialist in another nation (Chang, 2016).   

The relevance of the Research Problem

Literature 1 is appropriate for my research as it emphasizes on the Artificial intelligence in healthcare: past, present, and future. It is beneficial to understand the Adoption of artificial intelligence in health care. This research issue is strongly associated with my selected research concern (Schnurr, et. al., 2018).

Literature 2 is based on the Role of Artificial Intelligence in Health Care. It highly emphasizes on the detection of various chronic diseases and various automated systems and methods for avoiding the errors and controlling the disease progression. It is highly linked with my concern (Russell, et. al., 2015).  

Literature 3 is based on Big Data, Analytics & Artificial Intelligence: The Future of Health Care. It illustrated to access the information in order to care for patients in their home and their office. It also focuses on the adoption of artificial intelligence in health care. It is highly related to the chosen research issue (Peek, et. al., 2015).

Literature #	Research Problem	Methodology	Sub-problem	Relevance
Literature1	Artificial intelligence in healthcare: past, present, and future	Quantitative research methodology	Adoption of artificial intelligence in healthcare	Strongly relevant
Literature2	Role of Artificial Intelligence in Health Care	Qualitative research methodology	Adoption of artificial intelligence in healthcare	Strongly relevant
Literature 3	Big Data, Analytics & Artificial Intelligence: The Future of Health Care is Here	Mixed research methodology	Adoption of artificial intelligence in healthcare	Partial relevant

Under this section, I am going to use a methodology which combines with other methodologies. I have chosen certain research article that emphasizes on my research issues and also associated with my chosen problem that I required to interpret. I have classified the research methodology in several subsections that involve the research methodology with justification and demonstrate the limitation and pros of choosing the appropriate methodology. It also depicts the structure and process of completing the investigation (Lu, et. al., 2018).

Qualitative and quantitative research techniques

Although, I had previously discussed that I would like to execute the hybrid technology as it is not only limited to quantitative and qualitative research design but also emphasizes on the characteristics of both kinds of research design. As a result, it would be appropriate to use mixed research methodology as compared to using the single one strategy. I am going to apply the survey through a questionnaire to testing and collected response from employees who use the artificial intelligence technology in healthcare (Schnurr, et. al., 2018).

Benefits:

• Direct interaction with individual
• Assessment and survey would be implemented together  
• Genuine information  
• Interaction with users (Lu, et. al., 2018).
• Testing with evaluation of information   

 Limitations:  

• Different individual has their different views and opinion  
• Complex strategy for implementation purpose   
• Creation of vague results
• Adoption of artificial intelligence in health care could be difficult
• Insufficient budget with time (Chang, 2016).   

 

                                                                                (Sources: Chang, 2016)

From the above structure, it can be discussed that different approaches would be implemented in this research due to subjective nature of research concern. This methodology is effective to accomplish the research objectives.  In this project, I am going to use the inductive approach as well as survey through a questionnaire to complete the project in a methodological manner (Schnurr, et. al., 2018).   

References

Ashrafian, H., Darzi, A. and Athanasiou, T., 2015. A novel modification of the Turing test for artificial intelligence and robotics in healthcare. The International Journal of Medical Robotics and Computer Assisted Surgery, 11(1), pp.38-43.

Baig, M.M., GholamHosseini, H., Moqeem, A.A., Mirza, F. and Lindén, M., 2017. A systematic review of wearable patient monitoring systems–current challenges and opportunities for clinical adoption. Journal of medical systems, 41(7), p.115.

Beam, A.L., and Kohane, I.S., 2016. Translating artificial intelligence into clinical care. Jama, 316(22), pp.2368-2369.

Booth, R.G., 2016. Informatics and Nursing in a Post-Nursing Informatics World: Future Directions for Nurses in an Automated, Artificially Intelligent, Social-Networked Healthcare Environment. Nursing leadership (Toronto, Ont.), 28(4), pp.61-69.

Capone, A., Cicchetti, A., Mennini, F.S., Marcellusi, A., Baio, G., and Favato, G., 2016. Health Data Entanglement and artificial intelligence-based analysis: a brand new methodology to improve the effectiveness of healthcare services. La Clinica Terapeutica, 167(5), pp.e102-e111.

Chang, A.C., 2016. Big data in medicine: The upcoming artificial intelligence. Progress in Pediatric Cardiology, 43, pp.91-94.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H. and Wang, Y., 2017. Artificial intelligence in healthcare: past, present, and future. Stroke and Vascular Neurology, pp.svn-2017.

Lu, H., Li, Y., Chen, M., Kim, H. and Serikawa, S., 2018. Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), pp.368-375.

Mishra, SG., Takke, AK., Auti, ST, Suryavanshi, SV., and Oza, MJ., 2017. Role of Artificial Intelligence in Health Care. [Online]. Available at: https://www.tsijournals.com/articles/role-of-artificial-intelligence-in-health-care.pdf (Accessed: 7 June 2018).

Park, Y.R., and Shin, S.Y., 2017. Status and Direction of Healthcare Data in Korea for Artificial Intelligence. Hanyang Medical Reviews, 37(2), pp.86-92.

Peek, N., Combi, C., Marin, R. and Bellazzi, R., 2015. Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes. Artificial intelligence in medicine, 65(1), pp.61-73.

Russell, S., Dietterich, T., Horvitz, E., Selman, B., Rossi, F., Hassabis, D., Legg, S., Suleyman, M., George, D., and Phoenix, S., 2015. Letter to the editor: Research priorities for robust and beneficial artificial intelligence: An open letter. AI Magazine, 36(4), pp.3-4.

Schnurr, H.P., Aronsky, D., and Wenke, D., 2018. Medicine 4.0— the interplay of intelligent systems and medical experts. In Knowledge management in digital change (pp. 51-63). Springer, Cham.

UCSF. 2018. Big Data, Analytics & Artificial Intelligence: The Future of Health Care is Here. [Online]. Available at: https://newsroom.gehealthcare.com/wp-content/uploads/2016/12/GE-Healthcare-White-Paper_FINAL.pdf (Accessed: 7 June 2018).

Wang, Y., Zadeh, L.A., Widrow, B., Howard, N., Beaufays, F., Baciu, G., Hsu, D.F., Luo, G., Mizoguchi, F., Patel, S. and Raskin, V., 2017. Abstract Intelligence: Embodying and Enabling Cognitive Systems by Mathematical Engineering. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 11(1), pp.1-15.

Wong, T.Y. and Bressler, N.M., 2016. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22), pp.2366-2367.