What is Artificial Intelligence?
Artificial Intelligence (AI) is a widely used term that manifests through the use of algorithms based on machine learning. The technology driven by AI includes integrated platforms of data, robotic influence, chat-bots, voice recognition and many other features (Russell & Norvig, 2016).
AI is one area of computer science, which would emphasize on the creation of smart computing devices that would be able to work and react just as humans. The use of AI has thus become an essential part within the technological industry. The research within this field is entirely based on high technical grounds and requires high level of specialization.
The primary aim of the study on this research is to analyse the level of permeation of artificial intelligence within the field of healthcare.
- To understand the influence of AI within the various advanced form of technologies
- To identify the different challenges that are occurring for the development of AI within certain fields
- To provide explanation to some strategies that should be developed for mitigating the problems within the development of AI
The following research questions based on the proposal are:
- What is the current and future state of artificial intelligence in healthcare sector?
- What are the challenges with AI in the sector of healthcare?
The problem during the conduction of research is the selection of the list of hospitals who make use of AI within their internal systems and thus proceed with the research.
Artificial Intelligence (AI) could be defined as the capability of a digital computer to accomplish some tasks that could be associated with intelligent human beings. This term is generally associated with the projects based on developing smart systems that are endowed with the special intellectual characteristics. The AI based systems have the ability to reason, discover the meaning of algorithms and thus be able to learn from past experiences (Michalski, Carbonell & Mitchell, 2013).
There are some programs, which are based on certain high level of algorithms. These algorithms have the capability to attain the high level of performance similar to high level professionals and human experts. The use of high form of AI is diverse within computer search engines, voice recognition tools, medical diagnosis and handwriting recognition.
The questions based on the research would be very much helpful in identifying the purpose of conduction of research. They would also help in understanding the various aspects of the impact of AI within the healthcare sector and thus would conduct a thorough reseach on the selected topic.
There are some commonly used AI applications, which are evidence-based approaches that are mainly programmed by clinicians and researchers. The widespread adoption of AI within the healthcare sector is on a much higher rise and is thus able to solve several problems related to hospitals and patients (Acampora et al., 2013).
In the current state, AI based technologies are used for managing records, compiling and analysing of critical medical data. The digital automation is capable of collecting, storing and tracing of medical records of patients on real-time basis. There are certain mobile based applications, which are used to provide AI based medical consultation depending on the history of medical data and common form of medical knowledge (Amato et al., 2013). With the help of this application, users are able to report their symptoms within the application that makes use of speech recognition algorithms in order to compare against a pre-set database of illnesses.
In the recent times, there are certain gadgets such as Fit-bits and health monitoring applications that monitor the activity level and heart rate of the user. These wearable health trackers are able to send alerts to the users based on their activity and thus this information could be shared with doctors (Bhuvaneswari & Umamaheswari, 2018).
AI in Healthcare
There are some top level of applications within the healthcare sector with the use of AI based solutions. Some of the applications include:
Robot-assisted Surgery – The use of AI within the healthcare industry makes use of proper dorm of surgical experiences in order to improve the surgical based techniques. With the assistance of AI, a surgeon would be able to control the arms of the machine from a seat into a computer console situated near the operating table. This would permit the surgeon to perform the surgeries successfully and thus minimize the marginal errors (Guru et al., 2015).
Virtual Nursing Assistants – The AI based nursing assistants would be helpful in reducing the unnecessary visits to hospitals and would also lessen the extensive pressure upon the medical professionals. The AI solutions within the nursing units would be able to provide real-time answers and support, 24/7 health monitoring and quick access to medications. There are several bots, which are enabled to perform wellness checks with the help of voice and at lower costs (Erikson & Salzmann-Erikson, 2016).
Administrative Workflow Assistance – The use of automation within the workflow of hospitals would be able to prioritize various urgent cases and would also provide assistance to nurses, doctors and authorities in order to save time on regular tasks. Applications of AI technology includes voice-to-text transcriptions, prescribing proper medications and other facilities (Shortliffe & Cimino, 2013).
With the implementation and high dependence upon technologies, there are also several form of challenges, which are being face by the sector of AI within the healthcare industry. Some of the obstacles or challenges that are being majorly faced by AI within the healthcare sector are:
Technological Limitations – Although there are several benefits of using AI within the healthcare sector, yet there are some technical limitations within certain hospitals in implementing the AI based technology. Factors of cost are the major limiting factor within implementing AI enabled machines. There are still some factors such as the level of intelligence is not much advanced within the modern computers (Dilsizian & Siegel, 2014).
Medical Limitations – In some cases where image recognition, deep learning and machine learning are used for some purposes within radiology, there is always a risk of feeding the computer with thousands of images and other form of underlying technologies, which would be necessary for the purpose of medical benefits. The predictive abilities of AI enabled bots might be of not much use within resistance of treatments or side-effects from drugs.
Ethical Challenges – The legal and ethical issues based on AI technologies within medications would be able to overcome the challenges based on the technological limitations. Some ethical challenges that might be faced are based on the fact that who would be blamed for some mistakes if some smart algorithms would make an error or when they would make some false predictions. Other ethical implications would raise questions such as who would build the safety features based on certain rules and regulations (Cohen et al., 2014).
The main purpose of medicine is to eradicate the various diseases, which majorly affects humans. Various forms of emerging applications within AI and the recent form of trends within the sector would be able to full the aim of healthcare in achieving their ideal. The most noticeable themes within the use of machine learning within the field of medical research includes rehabilitation and wellness, prediction and prevention against diseases and technological augmentation based on doctors.
AI Applications in Healthcare
The applications based on AI might make use of user data from healthy populations. These collections of data would be able to accelerate with the integration of new devices that enter the market with the progress of technology (Topol, 2015). More level of insights are acquired with the help of sufficient data that would be available from healthy patients.
The prediction and prevention against various segments of diseases would mainly initiate from an extensive research within genetics and cells. This would be mainly aimed to exclude the prime causes of such dangerous diseases.
The research methodology would be able to explore the current and the future state of AI implementation within the healthcare industry. The ecosystem of AI within the healthcare sector would mainly be mapped by identifying solutions based on AI, machine and deep learning technologies. The main objective of this form of approach is mainly to predict the response based on behaviours and thus understand the ways in which the input variables would be able to relate with the gathered responses (Vasant & DeMarco, 2015).
The process of research would be mainly conducted by using primary data. The primary data would mainly consist of data that would be collected from several surveys and interviews. The doctors who are mainly engaged within the field of healthcare would be able to provide much insight based on the use of AI technology. The use of primary data would permit the researcher for conducting surveys based on which the analysing would proceed whether AI implementation would prove beneficial for improving the existing conditions within the medical field (Da Xu, He & Li, 2014).
The purpose of this research would mainly emphasize on the collection of primary data from different people who are specially based within the medical domain. A conduction of online survey would be made within some group of hospitals, surgeons and medical professionals. Based on the conducted questionnaire and answers collected, there would be an analysis of the data along with the proper recommendations.
The use of AI has intensified in the recent years. Implementation of AI within the healthcare sector would make a major difference within the sector as compared to the traditional based methods of performing operations and conducting treatments based on the type of complications within the health of patients. Analysis of huge amount of data collected from several sources would be able to lead the research towards a much efficient manner. The use of AI implementation would enhance the medical functionalities as the doctors would be able to perform surgeries and operations with high level of consultancy. Better form of decisions within the effectiveness of AI would lead to improved operational efficiencies, reductions of costs of machines, improvement of delivering better results and maintaining a healthy relationship among all the communities connected within the healthcare fraternity (Jiang et al., 2017).
References
Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013). A survey on ambient intelligence in healthcare. Proceedings of the IEEE, 101(12), 2470-2494.
Amato, F., López, A., Peña-Méndez, E. M., Va?hara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis.
Bhuvaneswari, R., & Umamaheswari, S. (2018). e-Health Care: A Techno Medical Revolution. Research Journal of Pharmacy and Technology, 11(3), 964-968.
Cohen, I. G., Amarasingham, R., Shah, A., Xie, B., & Lo, B. (2014). The legal and ethical concerns that arise from using complex predictive analytics in health care. Health affairs, 33(7), 1139-1147.
Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on industrial informatics, 10(4), 2233-2243.
Dilsizian, S. E., & Siegel, E. L. (2014). Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current cardiology reports, 16(1), 441.
Erikson, H., & Salzmann-Erikson, M. (2016). Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?. The Permanente Journal, 20(3).
Guru, K. A., Esfahani, E. T., Raza, S. J., Bhat, R., Wang, K., Hammond, Y., … & Chowriappa, A. J. (2015). Cognitive skills assessment during robot?assisted surgery: separating the wheat from the chaff. BJU international, 115(1), 166-174.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4), 230-243.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
Shortliffe, E. H., & Cimino, J. J. (Eds.). (2013). Biomedical informatics: computer applications in health care and biomedicine. Springer Science & Business Media.
Topol, E. J. (2015). The patient will see you now: the future of medicine is in your hands (Vol. 2015364). New York: Basic Books.
Vasant, P., & DeMarco, A. (Eds.). (2015). Handbook of research on artificial intelligence techniques and algorithms. Information Science Reference.
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