Evaluation System Framework of Artificial Intelligence Applications in Medical Diagnosis and Treatment

作者：Xueqing Tian, Haocheng Tang, Cheng Long, Zirui Liao, Yuxiao Li, Jing He, Ping Ren, Mao You, Zhen Pang · 发表于：Procedia Computer Science · 年份：2022 · DOI：10.1016/j.procs.2022.11.204 · 被引用次数：8 · 研究领域：Biomedical Text Mining and Ontologies、Artificial Intelligence in Healthcare、Artificial Intelligence in Healthcare and Education

Current medical artificial intelligence applications and products are confronted the dilemma of lacking standardized practical evaluation guidelines and management methods. This research adopts the Donabedian medical quality management classic model and the DeLone & Mclean benefit evaluation model to develop a basic framework of the medical artificial intelligence application evaluation system for auxiliary diagnosis and treatment and to design the corresponding evaluation procedures. This paper illustrates the overall project framework, followed by a detailed structure and construction process of the model proposed for fast and accurate medical evaluations, NHCKG. NHCKG provides a holistic view of diseases, medical regulations and evaluations. The proposed NHCKG can provide partial solutions to significant challenges faced by knowledge graphs and natural language processing (NLP). Based on the knowledge graph, the structure of a related medical question answering system is elaborated along with its advantages and disadvantages. The evaluation process can take this procedure as a blueprint for exploring standardized and practical evaluation of medical artificial intelligence applications. The process aims to build a concrete measurement basis for medical artificial intelligence products and to promote the healthy and stable development of the medical artificial intelligence industry.