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Paper short abstract

This paper examines how medical AI models are developed across publications, competitions, and commercial products in China’s AI industry. Based on ethnography in two startups and interviews with AI engineers, it shows how different institutional settings produce “the model multiple.”

Paper long abstract

This paper examines how machine learning (ML) models for medical image analysis are developed across different institutional settings in China’s rapidly growing medical AI industry. Drawing on ethnographic fieldwork at two Chinese medical AI startups, interviews with ML engineers and researchers, and analysis of published research papers, the paper situates everyday coding work within the infrastructures and organizational logics shaping contemporary AI development. I compare three settings in which ML models are developed and evaluated: academic publications, competitions, and commercial products. Each mobilizes distinct datasets, evaluation metrics, and development priorities. Academic publications rely on curated datasets and mainstream benchmarks to produce novel and publishable results. Competitions reward performance on highly controlled evaluation tasks and serve as arenas for demonstrating technical prowess. Commercial products, in contrast, require engineers to work with heterogeneous clinical data, integrate models into existing medical infrastructures, balance performance with hardware constraints, and navigate regulatory and market pressures. These differences give rise to what I call the model multiple: the same model architecture is enacted differently depending on whether it is built for papers, prizes, or products. While companies prominently showcase success in publications and competitions to signal innovation, everyday programming work is largely oriented toward product development. By tracing how models move across these sites, the paper shows how AI models are shaped by the infrastructures, institutional expectations, and data practices that sustain them.