Deconstructing the diagnostic reasoning of human versus artificial intelligence

• Published in: Canadian Medical Association journal (CMAJ) Vol. 191; no. 48; pp. E1332 - E1335
• Main Authors: Pelaccia, Thierry, Forestier, Germain, Wemmert, Cédric
• Format: Journal Article
• Language: English
• Published: Canada Elsevier Inc 02.12.2019; Joule Inc; CMA Impact, Inc; Canadian Medical Association
• Subjects: Algorithms; Analysis; Artificial Intelligence; Cancer; Clinical decision making; Cognition & reasoning; Computer Science; Data mining; Decision making; Deep learning; Diabetic retinopathy; Human health and pathology; Hypotheses; Internal Medicine; International conferences; Life Sciences; Machine learning; Medical diagnosis; Medical imaging equipment; Medical practice; Medical practices; Neural networks; Pattern recognition; Physicians; Reasoning; France
• ISSN: 0820-3946; 1488-2329; 1488-2329
• DOI: 10.1503/cmaj.190506

Human intelligence is evident in the concept of clinical reasoning, which has been defined as "the internal mental processes that a physician uses when approaching clinical situations." This central component of physicians'; competence, once honed, allows them to make diagnoses. In medicine, clinical reasoning is often understood from the perspective of cognitive psychology's information process theory. Artificial intelligence (AI) may refer to several different methods. Most AI diagnostics are based on machine learning algorithms that are "intelligent" enough to handle difficult and complex problems; algorithms rely on human intelligence for their creation. Recently, substantial progress has been made in this field through the resurgence of neural networks--a family of methods of machine learning--and particularly deep neural networks. Here, Pelaccia et al focus mainly on machine learning (specifically deep neural networks). They analyze the differences in the ways humans and AI approach diagnostic reasoning to argue that human reasoning will not become obsolete in medical diagnosis.