 The world of health care can be chaotic with all those prescriptions, diagnosis, treatments, expenses, and well, everything, but fear not, for some speculate that AI will help us navigate through the cluttered world of medicine. But is this true, and if so, are there any drawbacks to using AI in the medical field? Artificial intelligence enhances nearly every field it touches, with medicine being no exception. For those of you who haven't watched our previous video on AI and are a little close to what I'm talking about, AI is basically the ability of computers and machines to use features generally associated with intelligence in humans, such as learning, problem-solving, and reasoning to process data. In the context of medicine, this means AI can be used to help doctors recognize and diagnose diseases much faster and provide much more effective treatments for said diseases. For example, at Stanford University, a project which, as of the making of this video, is currently underway to develop an algorithm which can go through large volumes of medical data and predict which patients are likely to suffer from a fatal premature heart attack, thus allowing doctors to detect the problems earlier and increase the overall effectiveness of treatments. However, the idea of AI enhancing medicine is nothing new. A Stanford article published in 1996, which you can find below, talks about how neural networks could predict the likelihood of death from AIDS from a dataset of HIV patients much more accurately than other methods used at the time. So, based on what we've seen, it seems the ability of AI to detect and diagnose medical problems that are at times not visible to human senses at a rate much faster than what physicians can do is what excites many about its application in healthcare. But I think in order to truly understand the power of AI within healthcare, it's time we took a little bit more technical side of things and analyzed the application of neural networks in the context of healthcare. Alright, so we already know that neural networks can be used for diagnosis, but what other things can they be used for in the medical field? Well, neural network applications stem through a wide array of things such as biochemical analysis when it comes to things like tracking blood glucose or trying to calculate blood ion levels, or image analysis for things such as tumor detection or classification of tissues and vessels to determine how much an organ has matured, and much more. Additionally, neural networks are used in drug development to treat diseases like cancer and HIV, as well as modeling biomolecules. Now, you're probably asking what type of neural networks are used in healthcare because there are many types of neural networks that exist. Well, let's take a look at what seems to be the most, if not one of the most popular ones used in medicine. Cahinen neural networks. Cahinen networks are a type of neural network that we call self-organizing neural networks. Self-organizing neural networks take in data with multiple attributes and then create a two-dimensional visual representation of the data. Cahinen networks can be used to analyze medical data by clustering the data based on different factors such as the patient's blood type or medical history. For instance, a Cahinen neural network was used to cluster and analyze medical data from patients that did and didn't have COPD, based on factors like whether the patient had emergency room visits earlier, multiple medical problems, etc. The end result was that the analysis established a high correlation between being diagnosed with COPD and having respiratory symptoms coupled with other medical problems. The analysis also suggested that patients currently living with respiratory disease or a similar condition should be evaluated much more thoroughly for COPD. Aside from diagnosis, we can't talk about healthcare without bringing up the topic of cost. Now, there's a lot we can talk about with AI and healthcare costs, which seems like a good topic for another video. But long story short, things may be looking good with AI and the cost of healthcare. Economic experts claim that AI will help lower the cost of healthcare as its ability to detect problems earlier than humans, diagnose those problems more efficiently and accurately most of the time, and speed up the development of potentially life-saving drugs will save us loads of time and money. It seems like AI in the medical field could potentially be very beneficial for us. However, we might not want to get overhead of ourselves just yet, as critics of AI in the medical field do bring up some objections. For starters, critics fear that medical data used to train the AI models and create the algorithms may have some bias in it, which could result in skewed results when the AI model is used for real-world diagnosis. Furthermore, collecting medical data along with introducing a quote-on-quote third party as some say into the relationship between the physician and the patient could potentially destroy the patient's expectation of confidentiality and responsibility, which is kind of a big deal in healthcare. So ultimately, it boils down to two options, providing what may be cheaper and better healthcare, but at the potential cost of trust, confidentiality, and responsibility between the patient and physician, or we stick with our current healthcare system, but continue to maintain a good relationship between patients and their doctors. Let me know in the comments below which option you prefer and why. 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