Identifying patterns from data is another class of problems that computers are extremely good at. This is where you present the algorithm with large datasets (or evidence) and it is able to identify patterns in the data & fit statistical models to it, models that define the data.
In the case of medicine, these algorithms are able to see patterns in these datasets that a human doctor cannot. This is because the changes in data points are often subtle, spatially distributed and complex, escaping detection by visual inspection.
An impossible task for human senses. When repetitive tasks are represented with big data and rules, algorithms can be built and optimized to outperform human doctors at specific tasks. But, is that enough?
A doctor’s intelligence, however, is far more complex than mere rules and pattern recognition. It is obvious that to arrive at decisions and judgments one requires a very different mental process.
Rather than only learning from data, rules, and patterns, humans also use pre-formed observations and knowledge from first principles, reasoning, planning, creativity and intuition to arrive at decisions.
These algorithms, however fast and accurate they are at what they do, lack conceptual understanding of fundamental medical concepts and even basic reasoning to evaluate new situations. In some advanced AI implementations, they may be able to form hypotheses, but may still lack the ability to prioritize and test them.
There are also significant hurdles to overcome to get to this state. Seemingly, data is both the solution and the problem. Machine learning algorithms get better with more data they see, but access to this data, its privacy, inherent biases that may exist in the available set of data, remain points of concern. The more the availability, the better the algorithms work, the better the partnership works and better is the clinical outcome.
This also means that more trust from consumers and healthcare professionals is required to make data more available for research and development.
Doctor’s future tasks in the near future could include setting goals for these machine agents, designing them by modeling the foundational knowledge, formulate a hypothesis, perform evaluations and be the final authority in decisions and suggestions offered by AI.
AI will do what it is really good at, computationally intensive work that must be done to prepare the outcomes and suggestions for insights and better decision making in diagnosis and treatment plans. It is easy to put information and updates in a machine rather than human. Machines are designed to follow protocols and patterns, unlike humans which are unpredictable.