Artificial Intelligence in Medicine
What makes an algorithm intelligent?
Similar to how doctors are educated through years of medical schooling, doing assignments and practical
exams, receiving
grades, and learning from mistakes, AI algorithms also must learn how to do their jobs. Generally, the jobs
AI
algorithms can do are tasks that require human intelligence to complete, such as pattern and speech
recognition, image
analysis, and decision making. However, humans need to explicitly tell the computer exactly what they would
look for in
the image they give to an algorithm, for example. In short, AI algorithms are great for automating
arduous tasks, and
sometimes can outperform humans in the tasks they are trained to do.
In order to generate an effective AI algorithm, computer systems are first fed data which is typically
structured,
meaning that each data point has a label or annotation that is recognizable to the algorithm (Figure 1).
After the
algorithm is exposed to enough sets of data points and their labels, the performance is analyzed to ensure
accuracy,
just like exams are given to students. These algorithm “exams” generally involve the input of test data to
which
programmers already know the answers, allowing them to assess the algorithms ability to determine the
correct answer.
Based on the testing results, the algorithm can be modified, fed more data, or rolled out to help make
decisions for the
person who wrote the algorithm.
The above image shows an example of an algorithm that learns the basic anatomy of a hand and can
recreate where a
missing digit should be. The input is a variety of hand x-rays, and the output is a trace of where
missing parts of the
hand should be. The model, in this case, is the hand outline that can be generated and applied to other
images. This
could allow for physicians to see the proper place to reconstruct a limb, or put a prosthetic