Looking for software solutions to build your product?
Let's discuss your software solutions for your product in our free development acceleration call!
The use of artificial intelligence in medical field is becoming a reality in many specialties. Artificial intelligence (AI) is gradually becoming the main assistant of medical workers.
Machine learning algorithms help hospital administrators manage processes, doctors make more accurate medical decisions. In general, AI makes medical services better and more efficient. The introduction of AI-based systems is one of the key trends in modern healthcare.
These systems are handled a healthcare software development company – Glorium Technologies.
As a result of medical examinations, huge amounts of information are created. They contain a lot of graphical data that needs to be analyzed. These are MRI images, ultrasound results, cardiograms, and CT scan findings.
The process of analyzing and grouping medical images is time-consuming and labor-intensive. With the help of AI, the results of these graphic studies can be analyzed. AI technologies optimize visual information and help cardiologists and radiologists:
For example, the Arterys service makes it possible to pay more attention to patient complaints, as it saves the doctor from having to study in detail the results of images, ultrasound, MRI.
Algorithms analyze a lot of data at high speed and compare it with other studies of a particular patient. As a result, patterns are well traced, implicit relationships are established.
This process allows medical professionals to quickly track down important information. Reports are available in a convenient summary form, they allow you to develop a targeted and accurate diagnosis.
After receiving a medical image, the diagnostician analyzes it, reveals abnormalities or signs of the disease. To establish a diagnosis, the doctor must analyze several studies in a graphical form.
After machine learning, AI-based systems no longer only recognize medical graphics. They analyze medical images and report the features they find. For example, they are signal small neoplasms that the human eye may not fix. Such systems identify patterns and provide information about the characteristics of any deviations from the norm.
The diagnosis will be more accurate if the patient has several pictures taken at different times. Then the neural network will analyze the dynamics of the disease.
This leads to an increase in the number of incorrect conclusions. Unlike humans, a neural network constantly learns from its mistakes. Therefore, in the following series, he constantly improves the results. The computer was able to give correct diagnoses with an error of less than 3%.
Some doctors for rare childhood illnesses use IBM Watson neural network software. AI helps them make diagnoses with greater accuracy. To do this, the computer will look for relevant information in databases of clinics and medical journals. All this information is stored in the Watson Health Cloud.
Thus, every third person with a stroke receives the wrong treatment. Israel’s MedyMatch Technology has developed self-learning software to improve stroke diagnosis. To do this, MedyMatch compares the patient’s brain scan with a huge number of others that are in its database.
Patients can also use AI programs. The 23andMe software analyzes the genetic code and provides the client with information about their grandparents. So you can determine the tendency to certain diseases. Based on these data, patients adjust their lifestyles, and doctors establish more likely diagnoses.
In the development of AI-based software in the field of healthcare, the tasks of diagnosing and treating diseases are most in demand.
There was a shortage of medical professionals around the world even before the COVID-19 pandemic. According to the World Health Organization, for all the people of the globe to receive medical services, another 20 million top, and middle managers are needed. These figures are valid until 2030. In subsequent years, the situation will worsen for the following reasons:
These factors will certainly increase the need for highly qualified medical workers. The number of people who will have access to healthcare services will also decrease. Therefore, the latest developments should be based on artificial intelligence and medical knowledge bases. Such software will free physicians from daily simple work that takes a lot of time:
A huge number of medical studies and patient records are still stored on paper in an unsystematized form. This makes it difficult to find information and analyze it.
Based on this information, specialists will quickly receive accurate answers to their questions.
Due to the work of innovative software, doctors will be able to devote more time to:
The computer will provide all the information about the patient online, show its relationship with diseases or symptoms of other relatives. Based on the analysis, forecasts are obtained that will help to establish the disease in the early stages of its development.
Аi in the healthcare industry will be able to provide some assistance in the process of administering medical institutions. Although here machine learning is less effective than in the interaction between the doctor and the patient.
Nevertheless, artificial intelligence in the administrative areas of clinics can be used to process claims, organize documentation, manage income and expenses.
AI for healthcare will reduce the price of medical services, improve their quality, and provide treatment to more people.
Telemedicine is the trend of AI in healthcare 2023. Remote consultations increase the number of patients who can receive medical services. This is important for remote settlements and villages with few inhabitants.
Here, medical assistance is especially needed. In such applications, general practitioners can provide real-time recommendations for the treatment of diseases that are not life-threatening.
Many large companies are working on telemedicine software. Applications use artificial intelligence to capture and recognize symptoms. Then the program makes a preliminary diagnosis. After it recommends a specialized specialist to the patient. This reduces the number of working hours that doctors are forced to spend on non-core patients.
Some remote healthcare applications use AI with speech processing. Therefore, the patient asks questions at ease – as in a normal conversation at a doctor’s appointment. In this case, the patient receives a quick qualified consultation.
Therefore, a person does not need to use the Internet to determine the disease by his symptoms, he will receive high-quality advice from a virtual medical specialist. Also, virtual nurses make an offline appointment with a doctor.
Virtual nurses are already working in America’s clinics. They give advice, recommendations, other information. Computer assistants ask patients about their health, recognize symptoms. Then, according to these data, the optimal effective treatment is recommended.
They also tell patients how to take their medications correctly. Statistics show that 75% of patients have chosen to communicate with a virtual nurse, and not with a person.
AI makes telemedicine much more convenient. Machine learning is used for remote diagnosis, collecting medical research, and working with patient data. Online consultations significantly reduce medical costs.
Startups for the development of drugs (microscopic analysis, study of the effectiveness of drugs, the study of viruses, and the search for effective vaccines).
The development of a vaccine is especially important during the Covid-19 pandemic. Also, with the advent of new strains of the virus, it is necessary to conduct clinical trials faster. However, these are lengthy processes that require large financial costs. The use of artificial intelligence technology in healthcare significantly reduces the time for developing new drugs by several times.
AI analyzes the formulas of existing drugs and builds new ones according to several requirements. For example, with the help of AI, several drug options have been created to treat muscle fibrosis.
Computers solved this problem in 20 days. Then the experts chose the best drug options and tested them on animals for 24 days. So, it took only 44 days to choose the right medicine. At the same time, the standard drug development process takes about eight years.
AI technologies are designed to obtain data from arrays of information that a person cannot process. Neural networks are defining new ways to use existing drugs. This is the best direction for drug development companies, as the process is cheaper than creating new drugs.
Powerful computers made predictions about which drugs in development would be effective. This allowed reducing the cost of development.
The importance of AI in healthcare industry lies in the fact that products using this technology increase the accuracy of diagnoses, make doctors more accessible, and structure medical data. Software is being created with features that were not previously available – healthcare software development.
However, innovative technologies are now opposed by their high cost and people’s distrust of machines. In addition, many countries lack the funds to implement AI in healthcare. So, you need to create optimal software.
For example, simpler and cheaper AI systems will make medicine more accessible, while high-quality marketing and positive reviews will convince patients of the benefits of new software products.
Research also suggests that the market for AI in medicine will grow rapidly.
Artificial intelligence is the future, and it is coming today.
|cookielawinfo-checkbox-analytics||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".|
|cookielawinfo-checkbox-functional||11 months||The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".|
|cookielawinfo-checkbox-necessary||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".|
|cookielawinfo-checkbox-others||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.|
|cookielawinfo-checkbox-performance||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".|