There is a new and exciting application of artificial intelligence or better known as deep learning, that is being used in medicine. It has already been used to give insights into previously hard-to-understand cases, such as heart disease. Deep learning involves the use of large databases of medical images to identify which images show what features, and how to refine an algorithm to make a diagnosis. This allows doctors to take an image, look for any abnormalities, and then create a report that is tailored to the needs of the individual patient.
Medical professionals are discovering the usefulness of this new tech in medicine. If you ask an acquaintance in the medical field about artificial intelligence in medicine, they will most likely tell you about patients who have prescribed a particular medication but did not respond well to it. The doctor used the algorithms to find out why and then created a treatment plan to help the patient with improved results. In this instance, the patient did not get better because the doctor misdiagnosed the illness. However, the patient was able to get better because they had a better understanding of their bodies and the workings of the body.
A similar instance took place earlier this year when an elderly woman developed kidney stones. Despite numerous tests, it was not until the doctors inserted an artificial intelligence system called an ImageNet robot into her body that she was able to dissolve the stones. The doctors saw that the robot was able to recognize areas of weakness in her body, such as her kidneys, that no one else could see. The artificial intelligence identified the weakness and worked with the doctors to strengthen the weak areas. Without the ImageNet robot, the doctors would have been stuck with a list of details that did not match the final diagnosis. As you can see, this application of artificial intelligence in medicine uses data to make reliable diagnoses.
Another application of artificial intelligence in medicine is to improve the accuracy of medical treatments. Currently, doctors can only guess what type of medicine a patient needs. This guessing process can take hours or even days and is often wrong. The introduction of artificial intelligence into the healthcare industry will improve the speed at which doctors arrive at an accurate diagnosis. They will be able to look at a number of different examples at once and make a more reliable diagnosis. As time goes on, it is expected that the use of AI in medicine will reduce the number of unnecessary medical appointments.
Useful in neurosurgery
One other application is in the area of neurosurgery. During surgery, there are several things that the surgeon needs to do in order to successfully perform his or her job. However, if these tasks were being performed by a human mind, the surgeon would likely become overwhelmed and fail at his or her job. With the help of the brain-computer interface, this will no longer be a problem because the surgeon can simply let the computer do all of the work.
Help with diagnostics
These are just two areas where artificial intelligence will soon find a place in healthcare. In addition to improving the accuracy of disease diagnosis, they will also help alleviate other healthcare problems. Many people are worried about Artificial Intelligence in medicine due to fears that it could replace human doctors and be completely rogue. However, as long as the system is set up properly and kept updated, the potential for problems is minimal. There is always the chance that AI will be misused for evil but until now, the technology to do so has not existed.
It is important to understand how artificial intelligence is able to help improve the healthcare industry. To start, this type of intelligence will replace many routine processes in the field of medicine. For instance, medical specialists will one day be able to diagnose a disease with just a few images and data available to them. Before this was even possible, a doctor would need to go through a long series of tests and procedures. AI is a great improvement over this type of system, which helps to make the diagnoses more accurate.