Artificial Intelligence (AI) is one of the latest technologies that humans have ever invented. It is believed to have unlimited potential as it has better and stronger abilities than humans in terms of storage, analysis, etc. With its capabilities, it could provide endless opportunities in improving the quality of our medical treatment in the future. However, people still have huge doubts about the consequence AI might contribute when it is implemented to our medical system. This article will discuss the benefits/detriments of implementing AI technology in our medical system.
Beneficial of AI in medical treatment
Shortage of medical personnels is one of the major issues occurring in today’s medical system, AI robots could be the best solution to address this issue. Nurses aren’t able to continuously work for 24/7, but AI robots could operate nearly all day with little charging or maintenance time. Since AI can work 24/7, therefore they can monitor, and provide care for patients and even comfort patients to reassure oneself.
Accenture, a world leading service company stated, the clinician supply could not meet the demand and it will become worse for the following years (shown in Fig.1). With the aid of AI robots, the clinical demand could be met and appropriate medical treatment would be met on time.
AI robots could be an alternative use to nurses in critical situations where medical staff might be infected by the patients. This application has been recently used in Wuhan to take care of the COVID-19 patients in protecting the frontline-medical staff.
Prevention of fatal disease
Nowadays, fatal diseases such as cancer could be easily cured at its early stage. Around 9.6 million deaths could be prevented if they were diagnosed at an early stage. With the accessibility of big data, AI could easily identify patients that have risks of developing fatal diseases through their lifestyle, environment condition and other factors.
According to Business Insider Intelligence research, University of North Carolina Lineberger Comprehensive Cancer Center uses these AI to determine specific tumor treatment for over 1000 patients who were showing genetic abnormalities.
A recent application of IBM Watson’s AI consists of the ability to distinguish treatments for cancer patients from data collected by Google Cloud’s Healthcare app. This shows how AI robots could easily tackle the symptoms of certain fatal diseases and alert us before it’s too late. Study shows, the use of wearables technology in the US increased from 9% to 33% from 2014-2018 as the use of healthcare applications has increased significantly. The collection of medical data is quicker through wearable healthcare technology therefore it builds a strong foundation for AI technology. With the collaboration of AI through wearable tech, one’s health conditions could be easily monitored and ensured at their best condition.
Discovery of new drugs and treatment
According to PWC, it takes huge amounts of resources to develop new drugs and treatments based on current technology. They believed, with the intelligence and capability of AI, the resources could be greatly reduced in the future.
In recent years, many leading biopharmaceutical organisations have been trying to implement AI technology in medical treatment for diseases. For example, IBM Watson’s AI has been used by Pfizer to develop immuno-oncology drugs. It selects the appropriate compounds or organisms faster than humans which accelerates the process of drug development and therefore accelerates medical therapy.
The Protheragen organisation uses AI that learns large amounts of medical literature and data through Natural Language Processing. The AI analyses the structural characteristics and the relationship between drugs and diseases therefore effectively selects an antidote in a much shorter time.
Limitation of Medical Treatment
Personal data leakage
The Department of Medical AI based on Deep Learning + Big Data Systems needs a large amount of data to train learning algorithms. At present, the training samples of most AI products mainly come from various types of medical image data of patients from hospitals or some Healthcare apps, and small parts of data from digital records of human behavior. The identification and processing of sensitive information mainly depends on the consciousness of the enterprise. So, the privacy of patients might be divulged, which is not fair to patients.
In the application phase of deep learning algorithms, there are risks associated. Firstly, security, the algorithm has the risks of leaked and modified. When the algorithm is modified, the decrease in algorithm performance or the occurrence of errors will be difficult to detect. The medical field is closely related to personal safety. The consequences of such risks will directly infringe on personal rights. Secondly is the risk of algorithmic bias, the complexity and professionalism of the algorithm is difficult to explain at this stage how certain characteristics of the artificial intelligence algorithm input cause a specific output result. The bias of the algorithm may be the bias of the programmer’s subjective perception, or it may be the distribution of the input data itself that cannot represent the whole situation.
Ethical and legal problems
The AI could end up in legal issues if medical error occurred. There is an entire industry system behind a medical AI system once it is applied. Thus, many individuals and organisations would be involved, and the development of a practical medical AI system including programmers, medical counselors and hospitals. That will cause problems when investigating and assessing accountability and responsibility. Those potential ethical and legal loopholes could cost life during the application of medical AI.
The application of AI may cause an unemployment crisis in several fields. The AI robots may replace human experts both inside and outside the hospital. The medical diagnosis made by AI based on the big data and robot surgeons will cause unemployment to many doctors, anesthetists and nurses. This may also affect lecturers who train the medical personnel in the medical school. The unemployment crisis will cause brain drain since hospitals no longer need large groups of staff. The reduction in human medical experts in the short future will dramatically reduce the capability of handling emergency events, e.g. natural disaster that requires immediate medical support.
As engineers, applying scientific knowledge to every task is what we definitely aim for. The use of AI in medical treatment is practical from a technical perspective.Although many may say in future AI would replace the career of humans however we believe that AI would not supersede us instead it will guide and support us.