AI&SOCIETY - 14/01/2022

Artificial Intelligence in Medicine: Applications, Implications

The advancements in machine learning, AI and data analysis have opened up a lot of possibilities for advancement. By being able to accurately collect and analyze data we can speed up many processes, and create software that can be put to good use. This is especially true for healthcare where getting a swift diagnosis can save lives. Here we will go over a few instances of how AI is used in medicine and what these applications also imply. 

Recent Development 

One of the more recent inventions is the AI that can analyze chest radiographs. The reason why this is so useful is that it can effectively detect abnormal cell growth, which makes it possible to identify cancer. These can be hard to spot in the early stage which is why the software can be more precise than the majority of doctors. Of course, doctors are still needed in order to verify those results, so the software isn’t really taking over their jobs. The AI is a valuable assistant in this case as it can give a potential diagnosis based on the information it discovered. However, it’s always doctors that will have a final say.

Our organisms are complex and unique, which makes it tricky for machine learning to create a standard. But with enough information, the AI can effectively categorize the findings and then display different probabilities based on the analysis. The AI for cancer and tumor detection is actually very effective and gives precise feedback almost 99% of the time. 

Approval Methods

Any new tech, much like new medicine and treatment, needs to be approved, and these approvals do take time. If a tech is based on the existing model and serves as an update the process goes faster, but if it is brand new, it needs to go through a series of tests. This is necessary, but it also slows down potential solutions. This means that there are way more solutions out there that could be really useful but are potentially illegal. 

AI algorithms tend to be very complex and difficult to break down, whereas the approval process requires a lot of transparency. So, without effective means to explain how the Algorithm works, it may be impossible for it to get approval. Moreover, the market is really competitive, and uncovering how a program works, could lead to other companies replicating the process. 

Conclusion

AI can be extremely useful in healthcare but it does face its fair share of challenges when it comes to implementation. Moreover, some patients might be uncomfortable with the fact that the computer is handling their diagnosis. This means that there is a lot of untapped potential in this branch, and that progress might not be as fast as we would like.

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