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For many industries, especially those who heavily rely on improvements in technology to increase efficiency, Artificial Intelligence (AI) appears to be the next great breakthrough in order to streamline business practices thus increasing profits and overall efficiency. One sector which is fascinated by AI is healthcare. Unfortunately, there are many barriers to implementing AI in this sector. What are some examples of these barriers and can they be overcome?


  • Photograph Recognition Issues
  • Differentiating Algorithms Vs. Machine Learning
  • Relies Heavily On The Input Of Doctors


Photograph Recognition Issues


One of the hardest things for AI to do is to both view and interpret photos. In an area such as healthcare which relies heavily on scans and photographs of injuries to properly diagnose patients, this can pose a very obvious complication. The technology will have to greatly improve and be proven effective in other fields before being utilized to make decisions that have very real life and death implications.


Differentiating Algorithms Vs. Machine Learning


A common mix-up in the technology realm is between using a very complicated algorithm and using true AI. A complicated algorithm is the most common application in the healthcare sector. While this is highly useful, its is not AI in the form of machine learning. Machine learning involves taking data sets and then developing expanded conclusions based on the program’s ability to analyze it and look for patterns not easily seen by humans. It will be a difficult task for the healthcare sector to transition from complicated algorithms towards true AI.


Relies Heavily On The Input Of Doctors


A big advantage of artificial intelligence is the ability to recall information extremely fast in a healthcare sector where time can be the difference between life and death. AI is able to scan through thousands of different previous cases to see if there is a known treatment or cure to a similar disease in the past. The biggest barrier to this is that this requires massive data input from doctors in order to be effective. This means that, although the system may improve over time, the total start up time will be quite substantial. Only time will tell if AI can make the necessary improvements to fully integrate with our complicated healthcare system.