Artificial Intelligence (AI) in HealthcareIntroductionArtificial Intelligence (AI) has become the mantra of the success for the businesses in the 21st century. AI impacts all those things which connect with human life, e.g. human resource, communication, information and security management, healthcare, defence, manufacturing etc. (Tyagi, 2016). It is inevitable to understand the meaning of Artificial Intelligence (AI) before starting any discussion on AI. Oxford dictionary defines Artificial Intelligence as the intelligence exhibited by machines (Oxford dictionary, 2016). In computer science, the Machine falls under the category of AI when it simulates human tasks (Tyagi, 2016).
AI helps to create the smart solution for a product, process, or services by integrating the principles of biology, behaviour science, mathematics, engineering, and psychology to enhance the human intelligence (Hafiza, 2018). Though AI has the potential to increase the economic growth rates and boost profitability for any organisation, investment on AI varies by industry depending on the many factors like regulatory framework and industry’s ability to secure the private and public funding for the AI projects (Overton, 2018).
Healthcare sector acquired the most amount of investment in AI projects in 2016 (CB Insights research, 2017). As the healthcare sector seems an attractive spot for the investor of AI projects, in this essay, the focus would be on the AI in the healthcare sector. This essay will describe the historical evolution of AI in the healthcare sector, the current application of AI in healthcare, future trends of AI in healthcare, and challenges faced by the AI in the healthcare industry. Historical evolution of AI in HealthcareLate John McCarthy has invented the term Artificial Intelligence (AI) in 1956 (Rajaraman, 2014). The first problem-solving program, Dendral, was produced in the 1960s and 1970s using expert system methodology which provided the basis to create MYCIN. MYCIN is considered one of the earliest uses of AI in Healthcare. However, MYCIN was not used by general practitioners (GPs) on a routine basis. MYCIN used the simple rule-based algorithm. MYCIN type systems easy to create as it uses the simple rule-based program. Simple rule-based systems encounter difficulties while creating the software algorithm for more complex diseases or systems. The transition from simple rule-based algorithm to more complicated algorithm happened to solve problems associated with more complex illnesses (Roy and Das, 2019). In 1976, the computational analysis was used by the Scottish surgeon Gunn to treat acute abdominal pain, which was more accurate than the rule-based system. By the 1980s, AI development centres were established in the US, which helped to educate the world on the novel, and innovative AI approaches for the healthcare industry. Most of the AI systems, in place during this period, were using the expert system methodology. By 1990, Innovators have started using AI tools like Machine Learning (ML) and Artificial Neural Network (ANL) and started creating more advanced AI systems (Reddy, 2018). Application of AI in HealthcareAI is helping healthcare professionals to improve the efficiency and effectiveness of the healthcare facilities, diagnose the patients at an early stage of critical illness, treat the dangerous disease, and eventually, enhances the patient satisfaction by delivering the quality care (Mejia, 2019; Dickson, 2017).The smooth operation of the healthcare facility requires skilled staffs, which include doctors, nurses, biomedicals scientist, and technicians. Ageing populations, doctors and nurses’ shortages, and new diseases are raising the more demand for healthcare professionals than ever, which has resulted in a workforce crisis in the healthcare sector. AI provides the immediate solution to workforce crisis in the healthcare sector by facilitating electronic administration systems, automatic and faster diagnostics, and quicker decision-making (Meskі et al. 2018). Countries like India have designed the roadmap on digitalising all health care record system through automated healthcare record system (Srivastava, 2016). Nuance published case study is one of the best recent examples to demonstrate that AI supported system (Dragon Medical One cloud platform) helps the healthcare facilities (Nebraska Medicine) to overcome the challenge of complicated clinical administration process (Lancaster, 2016).Technologies invented with the help of AI is helping the healthcare professionals for the better planning of surgeries, quick analysis of medical images, and monitoring of the healthcare facility for adherence to the facilities policies and procedures (Mejia, 2019). The IBM Watson system developed for assisting the diagnosis of cancer. In the field of Neurology, AI system designed to restore the control of movement in patients with quadriplegia. Artery’s Cardio DL designed to deliver automatic, editable ventricle segmentations based on conventional cardiac MRI images (Jiang et al, 2017).Future Trend of AI in healthcareRoyal college reports, future of surgery, suggests that AI will transform the surgery for millions of people in next 20 years because of the significant breakthroughs in robot-assisted medicine, precision surgery, imaging, and specialised intervention in next 20 years. According to the report, robot-assisted surgery will get the generation next robot which will be more affordable, versatile, and portable; future imaging process will allow the surgeon to share the results during operations, and seek the specialist advice remotely; precision surgery will be more precise with the help of big data (Pitruzzella and Leahy, 2018).AI will extract more critical information from a patient’s electronic record in future, which will help the healthcare facilities to be more efficient. For example, currently, doctors spend lots of time reading letters, checking blood tests, and finding medical guidelines from various systems. In future, AI can help to generate the integrated system, and automatize the conversion of all recorded dialogue in the summary letters for doctors for approval or amend. This type of system would be easy to implement as it will assist clinicians rather than replacing them (Buch, Ahmed, and Maruthappu, 2018).UK National Health Services (NHS) has recognised the role of AI for future healthcare and started reviewing the current systems. As a result of this review, opportunities identified to leverage the AI at a greater extent and the workforce would be trained to drive the digital hospitals (NHS, 2018).Issue and challengesFull integration of AI in healthcare faces many challenges. The first obstacle comes from the complicated regulatory framework and lack of standards specific to AI design, development, and commercialisation activities. US FDA has published the guidance documents for the AI developers to overcome these challenges. Medical imaging platform designed by Artery is the first FDA-approved AI system that helps cardiologists to diagnose cardiac diseases. (Jiang et al., 2017). The second most debatable challenge is the concern of unemployment. AI offers opportunities to businesses to save money with digitisation. Implementation of digital processes can help to improve the efficiency and effectiveness of many methods which may bring profits to the companies but may lead to massive unemployment as machines may replace humans in many places. Though its negative side, it can reduce the price of the product for the consumers (Hafiza, 2018). The last critical obstacle to be discussed in the essay is the unique risk associated with the AI systems, hacking. Hacking may delay the surgery, compromise the confidentiality, integrity, and availability of the patients private information (Bresnick, 2018). National Health Services (NHS) system has been hacked in October 2018 due to one of the most significant cyber-attack which has caused a lot of inconvenience and cost around Ј92m to NHS as per the published report (Field, 2018). To overcome these type of challenges, regulatory agencies have already started publishing policies and guidance documents on how to mitigate the risk associated with system hacking during the life cycle of AI systems to assure that integrity, confidentiality and availability of the patients information (Bresnick, 2018).Conclusion:Based on all the above discussion, it is easy to conclude that AI has been helping and certainly would help in future as well to solve the many problems associated with the healthcare world. If we want to maximise the benefits of AI, current healthcare regulations, policies and processes must be revised in future to prevent the risks posed by AI in healthcare.ReferencesBresnick, J. 2018. 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