Machines such as computers, cars, smartphones, coffee makers, and digital clocks follow algorithms (coding instructions) created by computer science professionals to carry out their specific tasks. Artificial intelligence (AI) algorithms have the goal to make these machines able to mimic the problem-solving and decision-making capabilities of the human mind. In this way, they could not only execute actions smartly but also learn, predict and improve from them.
In medicine, AI algorithms have been increasingly present in softwares to analyze electronic medical records, healthcare management and clinical decision support systems, clinical data such as common tests and medical imaging, genetics and molecular biology research, biomaterials development, among others. In addition, they can be also physically found in medical devices and sophisticated robots used in the delivery of care or in surgery assistance .
Classic AI (also known as symbolic AI) algorithms are based on rules that were hard-coded into models (illustrated below). In this case, humans need to first learn the rules and then code the relationship into an algorithm. Time and resources are then needed to identify and continuously revise and update these relationships. One example of classic AI algorithms is found in clinical decision support systems which contain a knowledge base composed of rules and associations of compiled data. Among other things, these systems can be used to determine drug interaction with the patient. For that, a rule might be that “IF drug A AND drug B are taken, THEN alert the user of contraindications X and side effects Y.” The knowledge base can be continuously updated by users to include information on new drugs .