1.As technology has expanded, so has the increased use of algorithms to assist medical decision-making. These algorithms, known as "black box medicine," are not foolproof (example, the Optum algorithm found to be racially biased).

Medical algorithms leverage massive data sets in order to identify patterns and provide information or recommendations accordingly. Because these algorithms can analyze data more quickly than humans, they're capable of incorporating and adapting far more information than the average human's decision-making process can.

These types of algorithms aren't limited to medical use. Rather, they're changing nearly every industry, frominsurance claims processing to hiring decisions.

The term "black box medicine" refers to the fact that, in many cases, the algorithm itself is proprietary. Providers who use these algorithms to support decision-making don't see how the algorithm makes its recommendations or the data sets on which the recommendation is based.

The result is that the recommendations provided by the algorithm can be difficult to analyze for accuracy. For instance, when an algorithm recommends a particular dose of insulin for a diabetic patient, questions may arise: Why that dose? Is that dose appropriate, and if so, based on what criteria?

When a physician or provider uses AI as part of the decision-making process and harm results, who is liable?