The performance evaluation of the AI-assisted diagnostic system in China

Abstract

Artificial intelligence (AI) has been regarded as a major success in healthcare services. At present, few studies have successfully empirically analyzed this view through large-scale multi-center trails, especially in China as well as other less-developed regions. The research aim of this work is to empirically reveal how artificial intelligence benefits in less-developed regions and reallocates medical resources. This work takes the "non-perception-perception" public service performance evaluation model as the theoretical framework for evaluation, and the "task-periphery" performance structure model as the basis for forming performance indicators. This work has also adopted literature research, expert consultation, questionnaire measurement, and statistical inference as methodology, as well as a representative, advanced large-scale multi-center medical policy pilot case for performance evaluation. The case is conducted in the entire region of Puyang Prefecture, Henan Province, China. As of June 2024, 108 public healthcare institutions have been equipped with 291 modules and screened 281,663 people. 88.34 million RMB has been invested. A total of 493 questionnaires were collected (429 valid questionnaires). Based on the non-perceptual mode, the AI system has technical advantages, including more accurate diagnostic results (20.72% higher than the conventional rate), more detailed diagnostic data (precise to two decimal places), faster reporting (down to 0.2 seconds), standardized data collection procedures, unified healthcare collaboration platforms and lower healthcare insurance (reduced 85.7%-92.9%). Based on the perceptual mode, the overall performance value is relatively high (5.19/7 on average). The public value created by the system application is more distinct than the direct economic value. This advanced, representative, large-scale multi-center pilot case reveals that AI has effectively promoted data standardization, regional medical cooperation, and reduced medical insurance expenditures mainly by improving the accuracy, precision, and speed of diagnosis, enabling less-developed regions to access more efficient and fair medical resources. The application of the AI system not only creates very significant economic value for primary-level medical and health institutions, but also generates huge public value (sustainable development, social satisfaction, etc.). This work points out a referential path for the healthcare development in the Global South from the perspective of AI.

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