Much of the difficulty in reaching consistent evaluations of radiology and pathology imaging studies arises from subjective impressions of individual observers. Developing strategies that can reliably transform complex visual observations into well-defined algorithmic procedures is an active area of exploration which can advance clinical practice, investigative research and outcome studies. The literature shows that when characterizations are based upon computer-aided analysis, objectivity, reproducibility and sensitivity improve considerably. Advanced imaging and computational tools could potentially enable investigators to detect and track subtle changes in measurable parameters leading to the discovery of novel diagnostic and prognostic clues which are not apparent by human visual inspection alone. The overarching objective of this book chapter is to provide readers with a summary of the origin, evolution and future directions for the fields of automated image interpretation and computer-assisted diagnostics. The chapter begins with a high-level overview of the fields of image processing, pattern recognition, and computer vision followed by a description of how these disciplines relate to the more comprehensive fields of computer-assisted diagnostics and image guided decision support. Throughout the remainder of the chapter we have supplied multiple illustrative examples demonstrating how recent advances and innovations in each of these areas have impacted clinical and research activities throughout pathology and radiology including high-throughput tissue microarray analysis, multi-spectral imaging, and image co-registration.