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Conclusion to AI for Healthcare and key challenges

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Hi guys. In this video, I will be concluding my talk, and I will be giving you the information why preventive care is not so easy, why is hard, and what is the conclusion of my talks of previous presentation. So, as you see preventive care is more about just for prevention. And you really don’t know whether you are preventing, or you are just not getting the disease by luck. For example, if I say if you do physical activity every morning 20 minutes, you will not get cancer. So you start doing exercise every day 20 minutes. So how long you are going to do? One year? Two years ? Three years? So you need to just keep on doing it.
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Doesn’t matter whether you are one of the guy who will really get cancer or not. That is the point. And thing is, as I told, it’s repetitive and slow. We really don’t know. And the most important thing is, there is no pain in defense. You don’t see visible benefit of what you are doing. Maybe you feel like you are fit, you are doing good, but there is no pain or you don’t say, okay, because of this exercise I am not feeling this. Because of this repeatedly doing this particular thing, I am not getting this. So you just keep on doing the thing without waiting for the result. And that is the issue.
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And people don’t understand the probability that is statistics. If I say you get chance of 1.46 percentage of risk of getting cancer, what does that mean? So people really don’t understand. So you cannot explain them the probabilities or odds ratios for a common people. So we need to just keep on doing our best to prevent things. And science count produce reliable or useful predictions unless until it is proven. So, if you show this exercise have reduced the incidence of this cancer in this population, we cannot really give any evidence unless until we have such information. And basically we are not going to have such information because it takes so many years.
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And that is the reason why it has low market value, and that is why, what to say, insurance companies are not willing to pay for preventive care initiatives. But as ourself, if you are taking care of yourself, you really need to start taking attention on preventive care behaviors. And, in actually HIT, or a health information technology was key in Taiwan’s health care system. Because you should know Taiwan spend only six pointseven percentage of GDP, and they get, what to say, the satisfaction rate is above ninety eight percent I suppose. And AI, artificial intelligence and health care really can go hand-in-hand, because they are not two separate things.
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Because health care is all about information data, and AI can handle it very well. And AI can help on QPS, that is quality of patient safety, quality of care and patient safety. And as I told, it also helped us in personalizing the treatment and prediction and prevention depending upon candidate. And a high artificial intelligence has to change the future, will be changing the future of the medicine. Because we deserve it. So I hope, obviously, my previous video was bit longer, but I hope I managed to convey the message of why we need AI, and how we can use AI for better healthcare.
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And at the end, I sincerely request all my viewers please actively comment after watching the videos, so that we get feedback from you, and we can change our syllables or pattern of instruction in our coming videos. Thank you for watching the video.

This concludes the first session. We have talked about what is AI and its application in healthcare. Feel free to share some of the key points you have learned or any question you may have.

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Artificial Intelligence for Healthcare: Opportunities and Challenges

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