Artificial Intelligence-driven medical diagnostic systems for quality service delivery and business prospects - Spencer, Patience; Fiberesima, Alalibo Ralph (Faculty of Natural and Applied Sciences Journal of Mathematics, and Science Education, 2023)
Artificial Intelligence applications to health care service delivery including medical diagnostic services, is a well-accepted development as it mitigates the problems associated with diagnostic results optimization, diagnostic data processing time, and diagnostic information system availability. This study is therefore aimed at providing insight into the prospects of artificial intelligence–based diagnostic applications. Specifically, the ability of Artificial Intelligence-based diagnostic applications to increase the accuracy of disease prediction, efficiently classify diseases and determine their degrees of presence within the shortest possible time are discussed using exploratory and explanatory methods. In this light, artificial intelligence technologies suitable for the management of large and complex medical variables such as disease symptoms, signs, medical history, imaging results, and laboratory results, in a seamless manner are discussed. The business-oriented benefits as it affects Information Technology practitioners are also discussed. The significance of this study cannot be undermined because it provides an innovative diagnostic business idea for Information Technology Practitioners who wish to grant Information Technology support to Laboratory Technicians while delivering information that enables health care givers to select the right treatment and prescribe appropriate preventive or curative treatments for their patients. Considering the high demand for more effective diagnostic models for diabetes management systems, it is recommended that future work looks at the framework for the development of an adaptive artificial intelligence-driven diabetes diagnostic application.