A machine learning algorithm is used in many ways to identify incorrect or correct data that is fed into the system. It is first given some sort of a "teaching set" of data, which is then used to answer a question. As more and more questions are asked, this new information is added to the algorithm making it smarter and better at performing its task over time. So one can say that these machines are "learning." Here are seven of the most common uses of this technology.
Computers keep getting smaller and faster. That's been happening for decades. But almost all of them are programmed to do what humans want them to do, the way humans want them to do it, and nothing more. Now computers are beginning to learn -- on their own. Years of research into artificial intelligence are beginning to pay off.
Across all medical disciplines, artificial intelligence and machine learning will transform medicine beyond most people's imagination. Algorithms that help evaluate radiological images are just the beginning. AI could become an indispensable tool in all branches of medicine. The topic is also currently being addressed at the political level. China presented its Next Generation Artificial Intelligence Development Plan as far back as July 2017.
My views are similar to Jenny's here. It also depends what you mean by AI as it's a rather hyped term and I have sat in a meeting and heard someone describe it as'superior intelligence', which it is not. Machine learning and algorithms might be more appropriate descriptions and here the challenge is transparency and trust. We need to be confident that ultimately there is a clinician making an informed judgement in a patients best interest. That requires oversight as well as understanding how the algorithm works, the decision tree and the dataset used to develop/train the algorithms.
AI's ability to analyze X-rays, MRIs, and other scans has led it to be hyped up as the future of medical imaging. But patients remain reluctant to use it, as they believe only humans can understand their unique needs. Turns out they might be right. Many of the studies claiming AI outperforms doctors when interpreting medical images are poor quality and "arguably exaggerated," according to new research. The researchers warn that overhyping the power of these systems could lead to "inappropriate care" that poses a risk to "millions of people."