Medical Data Annotation and Artificial Intelligence Advancements in Healthcare

Artificial Intelligence can be a way to automate expertise. Still, creating an AI that is as accurate as a medical doctor is challenging enough. Of course, it takes the right programming and parameters. It also takes an awful lot of data. That data often comes from real patient medical data. So medical data annotation takes a great deal of care. Accurate AI has been trained with as little as two thousand MRI images, for example.

That’s amazing because machine learning algorithms for other applications usually need more than that to learn. The more useful data, the better. It is helpful that digital medical records and databases have been widely adopted.

Ethical considerations, rules, and regulations must be followed when handling patient information. The rules vary with country and region. Outsourcing data annotation projects to an experienced company can make a difference in ethical and regulatory compliance.

With everything right, AI can come to the same conclusions as medical doctors. The kinds of doctors with years of medical school and lots of experience. This is very like the AI doctors found in science fiction. They just don't have personalities yet. With more image data annotations, The accuracy of AI medical diagnostics will increase.

In recent years AI has excelled at the diagnosis of different types of cancer. There is also a worldwide shortage of doctors and medical staff. Medical doctors have been slower and more reluctant to adopt AI and trust its decisions compared to other scientists.

Adoption of AI in medicine when they are as good as doctors at diagnosing a long list of illnesses like cancer is needed now and in the future. AI can be very accurate, and doctors' distrust comes from the fact that after they've been trained, they act as black boxes.

Hopefully, they understand that it is like not trusting a calculator because it just gives you the right answer and does not show its work. It seems silly, but their concerns are serious.

That is to say, it is difficult or even impossible to understand how the AI came to its accurate conclusions or diagnosis.

AI is also useful for monitoring patients in hospitals. It can read the data from sensors and warn of any serious events like changes to a patient's health.

Because AI can monitor patients through sensors, it also has telemedicine applications.

Other kinds of telemedicine that benefit AI include mental health.

It is not perfect, though AI and various apps have increased access to mental healthcare. That can be so much more than a simple chatbot. This has come a very long way since the first attempt, ELIZA, was created in 1966.

Some people are more comfortable talking to an AI than a human about their mental health. Others may not be, and AI has still provided some benefit in expanding access to mental healthcare. That is important because there is a shortage of healthcare professionals of all kinds. There is also more demand from the heightened awareness and increase in mental health issues.

Using electronic health records and genetic information AI can predict health risks with 70% accuracy or more. This accuracy increases as this kind of medical information doubles every 5 years. That means that information doubles in both availability and amount. By now, there is probably much better accuracy for some AI.

Machine learning algorithms have also been proven in researching diseases like the coronavirus and Ebola. AI has been proven useful in researching chemicals for new medications. AI doesn't just help with coming up with new products for drug companies.

AI is good at figuring out all the many possible drug interactions. There are an awful lot of them. It can take information from a number of sources, including user-generated content. Company mergers can have their benefits as more information is also consolidated. That means more information is available to scale data annotation and the accuracy of medical AI.

AI powers surgical robots that aid doctors in many different fields of medicine and parts of the body. Everything from orthopedics (feet) to brain surgery and everything in between.

Recent and potential Medical Advancements from AI

• Accurate Diagnostics
• Patient health monitoring
• Telemedicine
• Predicting patient health risks
• Researching diseases like the coronavirus pandemic
• Researching chemicals for medication
• Finding all possible adverse drug interactions
• Increasing Doctor and staff productivity
• Automated Surgery Assistance

The Importance of Medical Data Annotation to Future Advancements in Medical AI

Although adoption by doctors is not what it should be, AI and robotics are still trending upward in medicine. That's because there has been a lot of innovation and exciting results. To achieve those results, machine learning algorithms need to be given a lot of protected patient information.

That information needs to be handled and cared for correctly. There are serious ethical considerations. Not to mention the severe consequences of not complying with regulations. Medical data annotation is a specialty in the highly technical and specialized field of machine learning data annotation.

Creating medical AI presents extra challenges because it impacts human health. Therefore, you should consider outsourcing and consulting with an AI data annotation service you can trust.