Different patients respond differently to different medications and treatment programs. Therefore, personalized treatment has great potential to increase patients' life expectancy. But it is very difficult to determine what factor should influence the choice of treatment. Artificial intelligence and machine learning can automate this complex statistical process. These algorithms help us understand what properties represent a particular response to a particular treatment in each individual. Therefore, we predict what possible reactions each patient will have to a particular treatment. The machine learning system learns this by referencing similar patients and comparing their treatment and treatment outcome. The predicted results help the doctors in this way and make the right treatment plan be designed.

We have said many times that we are now in the golden age of artificial intelligence, which is supposed to change all aspects of human life. Artificial intelligence in medical engineering and the entry of artificial intelligence into medicine in general is one of the new frontiers that artificial intelligence has begun to conquer and in recent years has made significant progress in this area .

The use of assistant robots for surgeons has become commonplace in special surgeries for several years. The use of assistant robots makes surgery less invasive and more accurate. Gene sequencing and editing is done with the help of artificial intelligence, which helps scientists find ways to treat various diseases .

One of the most important steps that artificial intelligence has taken in medical engineering and medical science in general is the changes that doctors make in the process of diagnosing the disease. In the near future, the process of diagnosis and treatment of the disease will be very different from what we see today. In the following, we will explain this fundamental change with an example .

Imagine for a second you were transposed into the karmic driven world of Earl. Your body temperature has not dropped and you have a fever and difficulty breathing. It is normal to see a doctor in such a situation as the rest of the time when you have a cold .

Imagine that this time when you go to the doctor, he will ask you about all the signs and symptoms you feel about your disease and all your answers and observations in a tablet and in a diagnostic software, which is one of the applications of intelligence Artificial in medical engineering and medical science, imports. The program will extract all the information in your medical record in a short time and prepare suggestions and recommendations to help diagnose the disease based on the symptoms of the disease you already have. The doctor then uses these suggestions and compares them with his or her personal assessments. Finally, based on all this information and evaluations, make the final diagnosis and tell it to you .

Proper diagnosis requires years of medical study. Even then, diagnosis is often a difficult and time-consuming process. In many cases, the demand for specialists is greater than the available resources. This puts pressure on doctors and sometimes delays vital diagnoses. For example, we can refer to the detection of corona prevalence in artificial intelligence.

All artificial intelligence systems used in medical engineering and medicine in general use deep learning algorithms to achieve diagnosis for the disease. The data available in medical centers and health care centers are used to train these systems. These AI systems then use special techniques to achieve the diagnosis. The challenge with deep learning techniques is transparency. The inputs and outputs given to the system are transparent, but it is not clear how artificial intelligence can diagnose the disease. Another point in this regard is that the quality of the artificial intelligence system in detection depends directly on the quality of the data .

Machine learning, and more specifically deep learning algorithms, have recently made great strides in medicine. These algorithms automatically detect diseases and make the process cheaper and more accessible. But how do machines learn to diagnose disease? Machine learning algorithms can learn to see disease patterns like doctors. The main difference is that these algorithms require thousands of interconnected examples to learn. And these examples need to be cleaned and digitized; Machines do not know how to read from the lines of books! Machine learning is therefore particularly useful in cases where physician test information is stored in a database. Here are some of these applications:

 

Diagnosis of breast cancer based on CT scan of the lungs

• Calculation of the risk of heart attack or other heart diseases based on cardiac signal (ECG) and MRI images of the heart
• Classification of skin lesions based on medical images of the skin
• Finding the symptoms of diabetes by examining retinopathy (retinal vascular disorder) from ophthalmic medical images

 

Due to the large and practical data in these fields, the developed algorithms have become skilled to the level of specialists. Here's the difference: Algorithms can detect in a fraction of a second. These results can also be used at affordable prices anywhere in the world. Soon, people all over the world will be able to use the highest level of radiological imaging at the level of the world's top specialists at a low price.

The use of artificial intelligence in medicine and the diagnosis of new diseases is the first step. Higher-level mechanisms are emerging, including: multi-source diagnostics (CT scans, MRIs , genomes and proteins, patient data, and even handwritten files). By analyzing a combination of these data, the disease and its course can be evaluated.

Artificial intelligence will not replace doctors any time soon! AI is not expected to completely replace doctors. Instead, artificial intelligence systems are used to alert physicians to potential injuries and dangerous patterns of disease. These warnings come to the aid of specialist physicians, who will spend their time and attention interpreting these warnings and timely signs.

 

Application of artificial intelligence in medicine and accelerating the development of drugs

The process of drug development is known to be expensive. A large number of analytical drug preparation processes can be optimized using machine learning. In this way, years of work and millions of dollars of investment can be simulated.

 

Making medicine with artificial intelligence and its steps

Pharmacy has four main stages that artificial intelligence has been able to enter in all of these stages:

• Step 1: Set goals for the intervention
• Step 2: Discover the candidate drugs
• Step 3: Accelerate clinical trials
• Step 4: Find the biological signs to diagnose the drug

 

Making artificial intelligence drugs: Setting goals for intervention

The first step in drug development is to identify the biological origin of the disease and its resistance mechanisms. Then you need to identify good targets for the treatment of the disease, which are usually proteins. There are many techniques such as short hairpin RNA screening and deep sequencing . These techniques collect large amounts of data to identify possible target paths. However, the old methods for calculating the large and varied volume of data sources and finding the path associated with them face many challenges. Artificial intelligence algorithms can more easily analyze all available data and can even learn to automatically identify good protein targets.

 

Making Drugs with Artificial Intelligence: Discovering Candidate Drugs

In the next step we need to find a compound that can react with the specified target molecules in the desired path. This step involves testing thousands or millions of possible combinations of compounds. In these experiments, the effects of these compounds on the target tissue as well as their side effects such as toxicity should be investigated. These compounds can be natural, artificial, or bioengineered (growing tissues made with the help of biomaterial engineers). However, the current mechanism is often inaccurate and leads to erroneous results. This process takes a long time to narrow down the options and reach the best drug candidates (usually drug leads). Artificial intelligence and machine learning also work at this stage. These algorithms can learn to detect the degree of conformity of a molecule. They make this identification from structural clues and molecular identifiers. They then choose from the millions of possible molecules the best options with the fewest side effects. This method saves a lot of time in drug design.

 

Making artificial intelligence drugs: Accelerating clinical trials

Finding the right people for clinical trials is difficult. If the wrong people are chosen, the testing process will be delayed and a lot of time and resources will be wasted. Machine learning can automatically speed up the process of designing clinical trials by identifying suitable candidates. Machine learning also ensures proper distribution of groups participating in the experiment. Algorithms can distinguish good candidates from bad ones. In addition, these algorithms can be introduced as a warning system for failed clinical trials. In this case, researchers are allowed to intervene in a timely manner and prevent the preparation of the drug.

 

Making a drug with artificial intelligence: Finding biomarkers to diagnose a drug

You can only treat the disease if you are sure of your diagnosis. Some methods are very expensive and require advanced laboratories and specialists, such as genetic sequencing. Biomarkers are molecules found in body fluids (usually human blood). These markers can determine with a high degree of certainty whether a person is ill or not. This will make the diagnosis process safer and cheaper. These markers are also used to determine where the disease is growing. In this case, doctors can more easily choose the right treatment and check that the drug works properly; But finding biomarkers for any particular disease is difficult and time consuming and costly. For this reason, we are once again seeing the use of artificial intelligence in medicine.

Artificial intelligence can speed up a high percentage of human work. These algorithms classify molecules into good and bad candidates. This allows experts to focus on analyzing the best results. Biomarkers are used to detect the following:

• Occurrence of the disease in the shortest possible time - a diagnostic biomarker
• Risk of disease in a person - a biological indicator of risk
• Probability of disease spread - Probability of disease progression - Preventive biomarker
• Probability of a patient responding to medication - predictive biomarker

Artificial intelligence has numerous applications in medicine and treatment. The meaning of these countless users is that from recognizing the connection between genetic codes to using artificial intelligence robots for difficult surgeries, everyone is among the users of artificial intelligence in the field of medicine. With all these applications, artificial intelligence has been able to create a modern course in health services and take it to another level.

 

Here are some examples of the use of artificial intelligence in medicine:

 

Simplify and streamline the treatment process for patients using artificial intelligence

In the healthcare industry, time is equal to money and capital. Providing an effective patient experience allows hospitals, clinics, and physicians to receive and treat more patients on a daily basis.

New AI innovations in the healthcare industry can improve the patient experience, as well as help hospital staff process millions of data points faster and more efficiently.

 

Collect and manage medical data and information using artificial intelligence

One of the next frontiers to be crossed by big data will undoubtedly be the healthcare industry. Valuable information is sometimes lost in the millions of data, causing the industry to lose hundreds of billions of dollars. In addition, the inability to connect important data points slows down the development of new drugs, the production of prophylactic drugs, and the proper detection process.

Many healthcare professionals have turned to artificial intelligence to prevent these losses. This technology has the ability to analyze millions of data in minutes and extract information from which we need to spend a lot of time to obtain.

 

Get help from robots based on artificial intelligence in surgery

In recent years, the use of robots in surgery has become somewhat popular. Hospitals use robots in many areas, from minimally invasive treatments to open heart surgery. According to a US clinic, robots help physicians perform complex treatments with precision, flexibility and control that go beyond human capabilities.

Robots equipped with cameras, mechanical arms and surgical instruments increase the experience, skills and knowledge of physicians to create a new type of surgery. Surgeons control these mechanical arms using a computer. The robot gives the doctor a three-dimensional view with a magnification of the surgical site on the patient's body, which was not possible before and doctors used to rely only on the power of the eyes. Finally, this robot can guide the surgeon and the whole team.

Robotic surgeries reduce the risk of surgery, and the patient will feel less pain after surgery. In addition, in robotic surgeries, the patient's recovery time is reduced.

 

Source: amerandish and fanology