Most people don’t realize that Artificial Intelligence is already in the healthcare industry and part of digital transformation. While robot doctors might seem to still be something out of a science fiction film, it won’t be long before many patients see AI playing a major role in their care.
The development of machine learning apps for a range of healthcare concerns is already here and we are starting to see AI assisting healthcare professionals in a number of ways. In some ways, the future is already here with the following applications of AI for medicine.
One problem with medicine is that people respond to drugs and treatments in different ways. A medication that might work well for one patient might be ineffective or have harmful side effects for another. As a result, doctors often have to use trial and error to find the right treatment for individual patients.
Machine learning can help to pinpoint the treatment options that will work best for each patient. AI algorithms can achieve this by referencing the patient against a wealth of data concerning other patients with the same condition. By identifying different characteristics and traits of patients that have received different treatments, the algorithm will be able to see which treatments offer the best solution based on the profile of the individual.
The diagnosis of some diseases can be time-consuming and tedious for doctors. In many places, there are not even enough experts that can diagnose certain diseases. This can lead to a backlog and long wait times. Machine learning is showing that it can be quite competent when it comes to diagnosing disease.
Skin cancer is one disease that AI could be good at diagnosing. Since early detection is usually done visually, a machine learning program could be trained to analyze images and diagnose different types of skin cancer. In one study, an AI system was able to detect different skin cancers with accuracy that was on par with that of dermatologists.
CRISPR provides scientists with the ability to edit genes with precision. This could lead to significant advances in gene therapy, but there are still issues. As examples, some applications might cause off-target effects and it is not always known how the snipped DNA will repair.
Using machine learning, it is possible to mitigate some of these concerns. AI could be used to predict the most promising location to target. Along with that, it has been shown that the effects of repairs can be predicted using AI algorithms. These applications could make gene editing more accurate and it could be used to predict the effects of different gene editing applications before testing them in the lab.
Developing new drugs is incredibly costly. In many cases, drug companies spend enormous sums only to find that a treatment is not viable. Even if a treatment does turn out to be useful and safe, it can take many years of testing and approvals before it can be used by doctors and patients.
With machine learning, drug companies can cut the cost of developing new drugs and reduce the amount of time it takes to get a drug on the market. AI algorithms could assist with things like identifying the most promising compounds to develop for different diseases or analyzing data from tests and clinical trials. Drug makers could even use algorithms to identify the best candidates for different stages of the testing process.
Artificial Intelligence is already touching off a revolution in the healthcare industry. It can diagnose diseases, help doctors pick the best treatment options, improve gene editing and cut the cost and time that go into developing new drugs. While these developments have the potential to improve care for many, it is just the beginning. Healthcare companies can researchers are investing heavily in AI, so we should expect to see these types of systems improving and becoming more common in the coming years.