The medical and health technology market has emerged some of the most innovative start-up companies in the world. These companies will help people to extend their lives and improve their quality of life. Their innovative technology is mainly driven by the emergence of software and mobility, allowing the health sector to digitally transform the original pen and paper operations and the current process of slowing the delivery of services. In this article, the author describes how to use artificial intelligence and machines. Learn to promote medical information.
Medicine is both an art and a science. Although doctors have received rigorous medical care to understand how the human body works, his understanding of all the decisions he has made, how to diagnose the disease, and how to choose the best treatment are all derived from some intangible measures: former patients. The experience accumulated years of observation and learning experience.
This is why the idea of ​​introducing machines into medicine seems impractical. A robot, no matter how well trained, can replace a doctor?
Machine learning is the most basic form of artificial intelligence and has penetrated into the medical field. It turns out that machines can play an important role in improving our health, including more accurate and faster diagnosis, finding better treatments and saving people Time and money prevent harmful side effects. In fact, as modern medicine increasingly relies on a great deal of research and drug selection and new information, machines may be better able to follow data and interpret data than human thinking.
Artificial intelligence in medicine is not intended to replace doctors (at least in the short term), but to increase the medical expertise of doctors. The AI ​​program has mastered the knowledge accumulated by a large number of outstanding physicians, including what is learned in medical schools and training, as well as the experience in actual medical care. AI scales these professional knowledge.
From disease symptoms to information on new drugs, the interactions between different drugs and the treatment of different people in the same way may have very different results. The amount of data available to doctors today is increasing. The ability to access and digest information is Quickly become the skill required, and this is what machine learning is good at. Harvard University professor of biostatistics Francesca Dominici said: "Doctors realize that if they want to know a lot of data, machine learning is a way to get them to learn from the data."
Harvard University is not the only academic institution that explores how people and machines can better integrate to leverage unprecedented medical information. At the University of Texas MD Anderson Cancer Center, the APOLLO program is screening genetic data generated by cancer patients and directing physicians for treatment, which will provide patients with better chances of survival for a longer period of time. Researchers at the Boston company Neurala are busy copying brain neural networks. Neurala's CEOSassimiliano Versace said: "Today we can design the brain with the complexity of mice. This is very smart." "Science and technology are now adapted to make artificial intelligence possible." In the field of mental health, startup companies Machine learning applications are being developed to help users detect symptoms such as depression or bipolar disorder.
The key to machine learning is the machine. The machines from IBM and Google learned the championship from the Jeopardy answer show. Chess masters and AlphaGo learned from the knowledge of previous players, which became part of machine programming.
Now, IBM is bringing medical knowledge into the medical field. The company is working with experts at the MemorialSloanKettering Cancer Center to develop three IBM Watson oncology products for addressing different types of cancer patients. One product will focus on providing patients with the best available information for cancer treatment; Watson provides a database, collects Dr. Memorial Sloan Kettering’s knowledge database, and the medical literature the doctors rely on in making decisions about how to treat patients. The most important cancer research.
The system includes the patient's symptoms and other significant information (such as his family history and stage of cancer) before the doctor provides three different levels of treatment options that can be considered. This includes standard treatment methods currently approved for cancer, treatments that are currently being tested but not yet approved, and treatments for other cancers. Finally, some early studies may suggest true experimental treatments. Different levels of choice give doctors and patients a treatment plan, and if standard therapies do not work, they can continue with more experimental treatment plans.
In addition to available therapies, Watson also assists advanced cancer patients who have exhausted standard therapies. For them, machine learning can call for clinical trials of potentially effective new therapies, including genetic solutions, which have just become a prospect in cancer treatment. Genetic options are based on careful analysis of patient-specific tumors, mutations that drive the disease, and drugs that may target these mutations. For human doctors, digesting all this information will be almost impossible, because doctors need time to see the patient and keep abreast of the latest developments on the site.
As more information about different cancer patients and their tumors becomes part of Watson, doctors will be able to see patterns that help them match specific patient profiles with survival rates and better outcomes. They will be able to recognize people with similar genetic tumors, for example taking different treatment routes with different health outcomes. This analysis can provide people with more accurate advice and which treatment approaches are most beneficial to the patient.
This system is still not perfect. Some of IBM's partners have found Watson to be troublesome in entering all the relevant information about the patient. Watson puts everything he knows about the patient into his treatment recommendations. But doctors support the idea that there needs to be a way to collect, sort, and classify the large amount of information each patient produces. This will be an important part of improving cancer care in the coming years.
This machine learning method has proved to be very useful in another medical field: mental health. One of the most important roles of psychiatrists and therapists for people suffering from depression and bipolar disorder is to help them avoid falling into difficult-to-recover emotions. Determining when people are most likely to suffer from depression or manic episodes may preclude them from psychiatric symptoms, and it turns out that in this situation, smart phones may do better than any psychiatrist.
This is because, as we all know, people who are depressed, or who succumb to feelings of sadness and negativeness, change their speech and behavior. They may speak less, and when they do, they will adopt a flat, monotonous tone. They may also be separated from friends and loved ones and interact less on social media. Even the best psychiatrist cannot follow all his patients and monitor when they begin to show changes in this behavior. But smart phones can.
Cogito is a mental health application based on machine learning and is currently being tested in Boston's Brigham and Women's Hospital. Once the app is installed on a smartphone, it monitors social media and phone activity to identify patterns of communication in order to detect the onset of depressive symptoms in users.
The application also includes a speech analyzer that can search for the effects of sound patterns and changes in pitch, which may be the first sign of depression. The AI ​​may better collect data over time and provide us with an individual's risk indicators for mental health issues and whether direct physician intervention is required.
Machine learning may be particularly helpful in alerting the doctor or the patient's family when it is out of control. Through technologies such as Cogito, we may be able to develop an early warning system that can monitor and see changes in behavior patterns for people with high risk of risk because they have a history of depression or suicide attempts. Determine when the risk is to prevent self-harm or dangerous activity.
Artificial intelligence can provide maximum benefits for people's health. Predicting the severity of a person's illness and understanding which treatments may be most effective may make machine learning an integral part of healthcare. We need to recognize that human capabilities combined with the capabilities of machines can maximize human health.
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