Factors influencing Chinese doctors to use medical large language models

The integration of medical large language models (MLLMs) into healthcare has garnered global interest, however, the determinants of their adoption by medical professionals remain underexplored. This study aims to elucidate the factors influencing doctors' intention to utilize MLLMs, encompassing both psychological determinants and demographic attributes.

An extended theoretical model was developed using constructs derived from the Technology Acceptance Model (TAM) and five constructs. A hybrid online and offline survey was conducted from March to December 2023, including 955 Chinese medical practitioners. Structural equation modeling was utilized to test the research hypotheses.

The measurement model exhibited satisfactory reliability and validity, with fit indices meeting scholarly standards. Perceived ease of use emerged as a significant predictor of both perceived usefulness and satisfaction. Content quality was identified as a substantial influence on perceived satisfaction but did not significantly predict perceived usefulness. Technical support and social influence were found to significantly affect perceived usefulness without directly impacting satisfaction. Perceived usefulness positively influenced both satisfaction and usage behavior, while perceived risk had a negative effect. A significant relationship between perceived satisfaction and usage behavior was established, with gender, age, education, and professional title moderating this relationship.

The study provides empirical evidence for understanding the adoption of MLLMs by Chinese doctors, offering management implications for future technical research, development, and implementation in the medical field.