Unlike their more state-of-the-art counterparts of huge languages, artificial neural networks require human contribution to learning and functioning.
Anns has existed since the 1950s. In the 1990s they began to deal with healthcare. Over the past 10 to 20 years, they have influenced healthcare. Today, Anns took a step up – many become huge language models, a neural network on steroids, so to speak. They are much more sophisticated. CHATGPT is the highest example.
Here’s why you need to know about Elderly School Anns now.
The retrospective analysis looks at history. A clinicist researcher can look back at the records of a thousand patients to find out if Dana Ann works. If the data say they work, it is encouraging. However, there are many variables in retrospective analysis that can affect the results.
On the other hand, with a prospective study, the clinicist researcher begins to look today and moves forward on time. This is a much more reliable form of evidence.
To sum up: over the past few years, much more prospective research on Ann has been conducted, including the most reliable prospective studies, called randomized controlled research (more on this subject below). And – here is Kicker – this kind of research is convinced by many clinicians who are worth using Ann today.
Paul Cerrarto is a senior research analyst and communication specialist in Mayo Clinic Platform, which focuses on finding digital solutions with many stubborn problems that health care faces. He is also a professor at Northeastern University D-Amore-Mc School of Business, where he teaches data extraction and machine learning.
Cerrato is widely published in the reviewed medical literature and has seven books about digital health. He was also recognized as one of the most influential bloggers by Himss. In addition, he was recently introduced to Sigma XI, Scientific Research Honor Society, which in the past included Albert Einstein, Linus Pauling and over 200 Nobel Prize winners.
He sat down with Cerrato to deeply delve into artificial neural networks.
Q: What is an artificial neural network and what is it doing?
AND. Ann is a set of nodes that are connected. The word node is brought to mind a neuron – and therefore the term “neural network”. They are very similar to neurons or nerves in the brain.
If you’ve ever seen a photo of a neuron in the brain as a central element with tiny tentacles entering and coming out of it – these tentacles are combinations. One neuron has a tentacle, connecting to the next neuron and the next neuron, so there is a network. In many respects, artificial neural networks are like neural networks in the brain, because they start with the insert, pass all these different nodes or neurons and ultimately come out with the exit.
Thus, the neural network is a set of nodes that go from input data to the exit to decide what problem you are trying to solve.
This is what a neural cut is doing. It analyzes data, regardless of whether it is an image or some other data, and then generates the result.
It starts with the training process. In the training process, let’s say you have these 10,000 photos. It can divide these photos into 5000 and 5000. The first 5,000 networks will analyze the data and develop a solution. This applies to the so -called ground truth. In other words, you inform the neural network the correct answer for each of these 5000 photos.
The network will make many mistakes, but it will come back and fix these errors, there and back, adapting these numerical values until it arrives at the fact that it is almost completely right in generating answers, melanoma or not melanoma.
This is the training part. Then the test part appears.
The second 5,000 photos undergo the testing process in the neural network. You see here how right the neural network is, answering the question, because in the second 5000 photos it was not told the correct answer. He must come up with it himself.
Depending on how right this process is, you will see if the neural network is worth using by clinicians. If the numbers are right in 80%, the doctor would be more willing to exploit it. But if it is 30% right, they will throw it away and say: “Well, it won’t really help me make a diagnosis.”
To sum up, during the training process, each picture is marked so that the network knows the correct answer. Then, during the testing stage, the network does not know the answer. This is a blind test that allows them to find out how right the network will be.
Q: What’s fresh in Anns, who convinced you to find out about them?
AND. One of the criteria that we exploit in healthcare to decide if something is worth is the amount of evidence to support the intervention. Over the past 10 to 15 years, we have written about neural networks, and many studies are the so -called retrospective analysis, as opposed to prospective analysis.
The most vital is retrospective analysis is a much weaker form of evidence. So there are fewer reasons why doctors and leaders think believe in the results when they are retrospective, unlike the future. Today’s research convince many doctors that these tools are worth using. [Editor’s Note: See introduction above.]
Artificial neuron networks they exploit Machine learning was used in several programs in Mayo Clinic. One of these Ann helps doctors performing a colonoscope improve their accuracy. There is something called the adenoma detection speed, which measures, how well Ann is able to detect pre -cancer polyps in the colon. This detection indicator has increased significantly since the exploit of Ann. Saves life.
The Eagle Mayo Clinic study is an example of a randomized controlled study that provides robust support for artificial neural networks. Such randomized controlled tests offer security that will assist determine whether the test results are reliable.
The Eagle study uses an artificial neural network in combination with ECG to check if this combination can assist detect a frail heart pump, which increases the risk of heart failure. Several clinicians received this AI, a well -known network and ECG. And then a separate group of doctors had a choice not to exploit AI.
Those who used artificial intelligence obtained better results. It is more likely that they indicate patients who were exposed to a frail heart pump. This is robust evidence that has convinced more and more doctors to exploit Anns.
A specialized type of Ann who drew the attention of everyone is a huge language model. He usually uses mass data sets, including billions of examples to generate a model (or algorithm), which can assist diagnose the disease, summarize patients’ records and respond to e -maile patients, if they are carefully constructed. On the other hand, if they come from their data from unreliable sources, they are susceptible to errors and sometimes it is known that they come up with answers referred to as hallucinations.
Anns and LLM are not only used for diagnosis. They are also used to summarize electronic medical documentation. The doctor enters the hospital room and has to find out what to do next for Mrs. Jones. The doctor opens her electronic health documentation and there are 100 pages of information. There is no way you read 100 pages of information. What you want is a concise summary of the most vital points on these 100 pages.
So electronic health documents are now connected to these artificial neural networks, including huge language models, and then generates a result, like the 10 most vital things for you. If Ann has been carefully constructed and approved, he can assist the doctor decide what to do next.
So they are used for diagnosis. They are accustomed to summarizing huge documents. They are even used to respond to the e -mailes of patients. Doctors are now responsible for answering the e -mailes of patients and are bombed with all these questions, and some of them can be answered via AI. If they are relatively basic.
For example, what are the side effects of the prescribed drug? Do I have to come for a follow -up visit? An artificial neural network or a huge language model can answer. Then the doctor can look at the answer, and if it is correct, send it with you. Or they can determine that it is incorrect and write the answer themselves.
So there are security in this. The doctor still has to review these things. But it saves a lot of time because the doctor does not have to write them all.
Q: Do you exploit artificial neural networks today anywhere in Mayo Clinic?
AND. First of all, we are not yet at the stage where we have a huge language model, which is available to clinicians and the audience. We are working on it. This is work in progress. What we have is artificial neural networks, for example for diagnosis of colon cancer, as a perfect example.
Take what an endoscopist or gastroenterologist sees when he or she puts the range into the colon to look for polyps. Usually, if the polyp is precancerous, it should be removed. Otherwise, it finally becomes cancer.
The problem is that you look through the telescope, you are a man with human eyes and you can always detect some subtle things that can be detected by a computer. Because the vision of the computer is much more sophisticated. The computer can look at millions of pixels and analyze all pixels Find something unusual that the human eye does not see.
What you see are two examples of very tiny, uncomplicated to skip adenomas or pre -cancerous polyps. And the computer emphasizes where they are in the colon and says: “Do you see these little things there? You can think about removing them because they can be forecast.”
Perhaps you did not notice it with the human eye. So it is a kind of program used by doctors in Mayo Clinic to improve the diagnosis of colon cancer. And the numbers are really good. Studies show that the agent detection indicator has increased since the exploit of Ann. Saves life.
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