General Theory of Information, Digital Genome, Large Language Models, and Medical Knowledge-Driven Digital Assistant

Abstract

Recent advances in large language models, our understanding of the general theory of information, and the availability of new approaches to building self-regulating domain-specific software are driving the creation of next-generation knowledge-driven digital assistants to improve the efficiency, resiliency, and scalability of various business processes while fulfilling the functional requirements addressing a specific business problem. Here, we describe the implementation of a medical-knowledge-based digital assistant that uses medical knowledge derived from various sources including the large language models and assists the early medical diagnosis process by reducing the knowledge gap between the patient and medical professionals involved in the process.

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