Among the 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 working on large AI projects. Machine learning technology has been widely used in medical insurance claims, clinical decision support and judgment radiography.
According to reports, 86% of hospitals in 2017 are using some form of AI. And medical institutions such as the New York Presbyterian (NewYork-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 hottest emerging careers, and early practical applications include insurance claims, clinical decision support, cybersecurity, 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 radiographs, it is unethical to continue to make people do this work. Of course, this issue remains to be seen, and there is no clear answer in the short term.
Dr. Kyu Rhee, MD Watson Health, presented the three principles of AI: purpose, transparency and skill. The purpose is to help humans instead of replacing them; suppliers must transparently disclose the training process of algorithms and AI systems. Rhee calls the new skill set required by AI a human + AI (Human + AI) because they are enhancing people's abilities.
This applies to clinicians, administrators, and health IT professionals. In fact, 2018 will be a good time to learn more about the future of AI, which, according to Accenture, is reorganizing the modern concept of medical services. The company predicts that the deployment of AI tools will increase dramatically to $150 billion in 10 years. Hospitals and IT departments clearly have significant risks in terms of budget. Experts began to predict that AI, machine learning, and cognitive computing are moving in the direction of similar network bubbles and the eventual bubble burst. Hospitals should carefully consider how to avoid false investments and introduce cutting-edge technology to improve or save patients' lives. Learning from other industries is a good start. The return on investment in AI was also initially formed in 2017. In fact, AI is a new investment return model, so it is even harder to persuade corporate executives to invest a lot of money in this emerging technology.
The report believes that the health care industry has made great progress in AI in 2017. The lack of best practices shows that there is still a lot of work to be done. Looking ahead to 2018, the hospital will further differentiate its hype, AI, cognitive computing and machine learning, and use AI tools to make surgery and care work more efficient and safer.
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