What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects of seeking optimization in the problem-solving process. Using these parameters as a heuristic for evaluating the capacity of AI to address issues of diagnostic error through design, the paper presents a conceptual review of the discipline of AI in medicine. Studies relying on procedural rationality describe models that treat diagnosis as a “natural artifact” by employing symbolic methods designed to simulate human problem-solving. Research adhering to probabilistic rationality describes models that treat diagnosis as a “natural artifact” of an ecological image by utilizing sub-symbolic methods designed to simulate neural networks. Research guided by situational rationality describes models that require treating diagnosis as a “socio-cognitive artifact,” the artificiality of which is organized in discourses of patient-centered decision-making. The paper concludes with a commentary on the ethical application of AI in health and medicine, given the logical differences underlying diagnostic task performance.