Exploring the lmpacts of Al Technologies on Medical lnstitutions with Knowledge Graphs

Abstract

Artificial intelligence (AI) technology and its applications have been widely implemented into various sectors of our life. As a pillar industry of national economy, medical and health care has been intertwined and merged with AI technology, leaving profound socioeconomic outcomes with the broad masses of the people. Therefore, it is important to analyze and cope with the influences of AI technology on the operation and development of medical institutions. "AI social experiment" as a real-world research approach that focuses on the practice and social impacts of AI technology is becoming an emerging paradigm. The current research methods of AI social experiment have two disadvantages. First, manual pre-processing is primarily used when dealing with raw text data, resulting in low efficiency overall. Secondly, the workflow based on manual pre-processing has poor robustness, which means that any slight change in research needs can lead to major adjustments or even overhaul of the entire work. This study innovatively applies the knowledge graph technology to the AI medical social experiment and proposes an optimized ontology for this scenario. Through experiments, we verify the feasibility of the method, and look into the prospects of its generalization.

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