NURBN1005 Artificial Intelligence in Healthcare Assignment Sample

• Subject Code : NURBN1005
• University : Federation University My Assignment Services is not sponsored or endorsed by this college or university.
• Subject Name : Medical Sciences

Introduction to Research and Evidence-Based Practice 

Introduction to Artificial Intelligence in Healthcare

In the era of growing demand and completion, expansion and development of technologies are taking place at elevated pace. Artificial technology (AI) is one of the best inventions of human kind and its significance is increasing in all industries. Many industries rely on AI and some of them cannot imagine their business operations without it. Recently, many healthcare industries want to establish Artificial intelligence to boost their performance and productivity. AI refers the situation where a robot or mechanical machines are capable of making decisions like humans by learning and analyzing. It is also known as machine learning (Haung & Song, 2015). The aim of this report is to discuss the positive impacts of Artificial Intelligence in healthcare industry.

Literature Review of Artificial Intelligence in Healthcare

Application of AI

Innovation and development of new software and hardware technologies, AI is used for various purposes such as:

• Iot (Yang, Jinag & Xie, 2018)
• Machine Vision (Gua, Liu & Lew, 2016)
• Autonomous Driving
• Natural Language Processing (Alsharani & kapetanios, 2016)
• Robotics

Many researchers in biomedical field are making an effort to apply AI for enhancing analysis and treatment results and boost the productivity of healthcare industry (Beam & kohane, 2018). It is use for medical imaging diagnostics which enable the doctors to view inner workings of the body a patient for treatment, for example- X rays, CT scan and MRI machine. It also helps the surgeon in making effective decision by examining the diagnosed image. AI can also assist in complex surgeries with the help of well equipped robots, which helps in identifying the risk and minimizing them by providing relevant data. Development of AI can also help in taking proactive medical care with the assistance of apps that will take the patient’s information and give relevant results. With the help of these apps patients can also order their medicines at the convenience of their house.

Benefits of AI

• AI helps in making predictive medical decisions by analyzing all the symptoms and information of the patients followed by medical treatments and suggestions, especially in the field of biomedicines. It can help in predicting cancer at early stage and also the chances of survival (Dangi, Kumar & Shukla, 2018).
• It enables to make advanced treatment plans and methods. Now most complex surgeries can be performed with the assistance of a robot. Other examples are wound healing by printing skin cells, 3D printing of human body parts, blood vessel reproduction is also possible now.
• AI ensures cost effective and affordable medical treatment for both doctors and patients through online medication services and drug development.

Disadvantages of AI

• Lack of security of information as systems can be hacked.
• Patients usually do not trust the machines doing their surgeries. On the other hand, doctors might feel threatened as machine could easily replace them in future.
• Machine performance can go wrong when wrong instructions are given.

Conclusion on Artificial Intelligence in Healthcare

There are many fields in which AI can be helpful for both patients as well as the doctors. It can contribute so much in biomedical field; robotic surgeries are possible and provide affordable medical treatments. AI helps in boosting productivity and better outcomes can be seen. There are some areas which need focus for improvement but overall AI can make a huge positive impact in healthcare industries.

References for Artificial Intelligence in Healthcare

Yang, K. Jiang, D. Zhao, C. Yu, Z. Cao & S. Xie, (2018).Intelligent and connected vehicles: current status and future perspectives. Sci China Technol Sci, 61(10) (2018), pp. 1446-1471

Huang, G.B. Huang, S. Song & K. (2015). YouTrends in extreme learning machines: a review.

Neural Netw, 61, pp. 32-48

Nguyen, L.M. Kieu, T. Wen & C. Cai. (2018).Deep learning methods in transportation domain: a review IET Intell Transp Syst, 12(9), pp. 998-1004

Chiang & T. Zhang,. (2016).Fog and IoT: an overview of research opportunities IEEE Internet Things J, 3(6), pp. 854-864

Vashistha, A.K. Dangi, A. Kumar, D. Chhabra & P. Shukla,. (2018).Futuristic biosensors for cardiac health care: an artificial intelligence approach. 3 Biotech, 8(8), p. 358

Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu & M.S. Lew,. (2016).Deep learning for visual understanding: a review Neurocomputing, 187 (2016), pp. 27-48

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