Clinician Cognition and Artificial Intelligence in Medicine

There is a myriad of reasons for there to be a more intelligent paradigm in medicine with the adoption of artificial intelligence: augmenting clinician knowledge and expertise; decreasing clinical and administrative burden; facilitating care coordination; and mitigating clinician burnout. There is a concomitantly long list of challenges for artificial intelligence adoption that pertain to data and databases, technology, stakeholders, and other issues such as bias and ethics. Clinician cognition will be more important than ever before with the advent of artificial intelligence. The clinician’s brain has several elements: perception, cognition, and operation, and these are used in various proportions depending on the subspecialty. Aspects of clinical medicine such as complexity, uncertainty as well as biases and heuristics will be additional challenges for medicine in the future. Evidence-based medicine and its limitations are discussed in the context of a new paradigm of intelligence-based medicine. Current applications of artificial intelligence in medicine and health care such as medical imaging, decision support, precision medicine, and altered reality, and robotic technology are briefly discussed.