As IT field has improved significantly over the past years, AI influence on another field can started to be seen as well. Especially in medical field, where new diseases keep showing up. However, ever since AI take part in, it gives doctors an edge in identifying those diseases. At biotechnology firm FDNA in Boston, Yaron Gurovich and his team, has found similarities in person that is likely to have genetic syndrome (Whyte 2019). They use neural network to gather information and look for pattern in people with syndrome through genetic and facial recognition. It is proved to be 91% accurate. This will help doctors to pinpoint the cause of the syndrome and probably possible method to prevent child to develop syndrome or maybe way to reverse the syndrome. A study from 2016 (Mendeley 2018) shows that physician spend more time on data entry more rather than spending time with the patient which is crucial. The way doctors treat patient have also changed as AI are getting more involved. For example, the daVinci robot which reduce risk of operation where instead of the doctor that do the incision; it is now the robot, which is still under the doctor control but with more precision that means there is no hand tremors thus less risk (Tomlinson 2018).

Secondly, it also opens up new jobs position. As AI’s help is advantageous in medical field people/ doctors need to be properly trained before operating this new instrument. Example of a machine that needs to be with much care is x-ray-machine. X-ray machine is used to see through layer of tissues of human’s body. To check any fault within the body that can’t be seen from the outside. An x-ray operator needs to be properly trained as x-ray machine radiates beams that passes through the body to produce images of the insides of the body (Wonderopolis 2019). Excessive exposure to x-ray radiation can cause cell mutation that may cause cancer (Mayo Clinic 2018). However, the benefit that x-ray offers outweigh the risk and the effect from the radiation varies between person and is also affected by the age. Still, x-ray machine is dangerous as it emits radiation and only properly trained radiographer can operate it. This is the case if human is the one that operate the machine. Test has been conducted where scientists used algorithm to detect different pathologies and compare it with radiologist (Armitage H 2018). Out of 14 pathologies the algorithm is able to analyse 11 just as good as the radiologist.

These being said, there are still risks that come when involving AI in health care. AI is still in process of learning which mean that it is still far from being able depended. Implementing AI in health care now will only just leads to mishap. The amount of data there are in health care is tremendous and AI does have the ability to learn and store lots of data, however, it takes time and many tests to confirm its reliability (Garvin 2017). It can be assured that AI will become better and sophisticated in the future, but it is not recommended to be fully implemented in the current time. As it takes time to gather all the data and lots of field test for its reliability, means it will also take fortunes in the process to realize this. At the end of the line AI will just be used as a tool to ease jobs for doctors.