Beginning with the first attempts to develop computerized systems in medicine and biology, it was recognized that one of the significant obstacles was the inherent uncertainty which arises as a natural occurrence of these types of applications. Unlike physical science applications, the life sciences are not amenable to straightforward algorithmic solutions for a number of reasons, including lack of complete understanding of mechanisms in biological organisms, inability to obtain complete information regarding the state of the organism, lack of precise ranges of normal values for physiological parameters, and complications caused by the interaction of several physiological systems functioning simultaneously. The most complex of these organisms, the human, is the most difficult to model. In fact, most efforts have been toward developing medical decision-support systems. Because of these inherent uncertainties, creative approaches were sought to develop both modeling and decision making systems in the life sciences. Initially, statistical and pattern recognition approaches were tried, followed by a decade of research into knowledge-based systems. Neural network modeling was also used beginning in the late 1960s. This approach met with little success but reemerged in the 1980s, with new algorithms and faster computers, to become one of the primary approaches in medical decision making. Although the early systems recognized the presence of uncertainty, it was usually dealt within an ad hoc fashion. Only in the last fifteen years have techniques from fuzzy logic been applied extensively in medical systems. However, in that short time, these systems have made significant inroads in modeling complex problems and producing practical decision support systems.