In many innovations, there are many practical artificial intelligence (AI) applications in the medical field. Almost all hospitals are already using some form of AI, and many medical institutions are already carrying out large-scale AI projects. Machine learning technology has been widely used in medical insurance claims, clinical decision support, and diagnostic radiographs.
According to reports, 86% of hospitals are using some form of AI in 2017. And medical institutions like the New York-Presbyterian have launched large-scale AI projects. There are now two types of machines that are supervised and unsupervised. Machine learning engineers are one of the most popular emerging professions. Early practical applications include insurance claims, clinical decision support, cyber security, and radiology.
AI also raises questions about ethical and emotional intelligence, that is, medical institutions and non-medical companies need to establish standards, obligations, and indicators before deploying technology. In addition, if machine learning algorithms prove to be more effective than humans in reading radiograms, it is immoral to continue to let people do the work. Of course, this issue remains to be seen, and there is no clear answer in the short term.
Dr. Kyu Rhee, Health Minister of IBM Watson Health, put forward the three principles of AI: purpose, transparency and skills. The purpose is to help human beings instead of replacing them; suppliers must transparently expose the training process of algorithms and AI systems. Rhee calls the new skill set required by AI Human + AI because they are enhancing people's capabilities.
This applies to clinicians, managers, and health IT professionals. In fact, 2018 will be a good time to learn more about the future of AI. According to Accenture, these technologies are reorganizing the modern concept of medical services. The company predicts that the deployment of AI tools will increase dramatically to US$150 billion within 10 years. Hospitals and IT departments obviously have a big budget risk. Experts began to predict that AI, machine learning, and cognitive computing are moving in the direction of a similar network bubble and the eventual bubble burst. Hospitals should think carefully about how to avoid misinvestment and introduce cutting-edge technology to improve or save the lives of patients. Learning from other industries is a good start. The return on AI investment was also initially formed in 2017. In fact, AI is a new model of investment returns, and it is therefore harder to persuade corporate executives to invest heavily in this emerging technology.
According to the report, in 2017, the healthcare industry made great progress in AI. The lack of best practices shows that much work remains to be done. Looking into 2018, the hospital will further differentiate hype, AI, cognitive computing, and machine learning, and use AI tools to make surgery and nursing work more efficient and safer.
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