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Breakthroughs have been achieved recently in the application of artificial intelligence (AI) for the eye disease screening in Chinese primary healthcare institutions, but challenges have also emerged. First, AI software has continuously evolved, expanding the range of eye diseases that can be screened, enhancing diagnostic accuracy, and progressing towards predicting the course of eye diseases. However, inadequate infrastructure such as 5G and a shortage of specialized personnel have hindered the coverage of screenings. Second, while the cost-effectiveness of AI is well-established, new screening models have impacted the equity of screenings. It is essential to tailor AI application models to the specific context of China. Third, AI screening guidelines have been increasingly improved, providing direction for AI development and reference for the promotion and application of AI technologies. Nonetheless, high-quality empirical research is urgently needed to provide scientific evidence for policymaking related to AI in the eye disease screening. Therefore, it is suggested to develop multimodal AI models that integrate basic data such as symptoms and medical history with simple ophthalmic examinations, to accelerate the construction of infrastructure like 5G and focus on cultivating interdisciplinary talents, to explore suitable service systems and models for the large-scale eye disease screening tailored to local conditions, and to conduct long-term, multi-center, empirical studies.

The Altai Mountains (ALE) and the Greater Khingan Mountains (GKM) in northern China are forest regions dominated by coniferous trees. These geographically isolated regions provide an ideal setting for studying microbial biogeographic patterns. In this study, we employed high-throughput techniques to obtain DNA sequences of soil myxomycetes, bacteria, and fungi and explored the mechanisms underlying the assembly of both local and cross-regional microbial communities in relation to environmental factors. Our investigation revealed that the environmental heterogeneity in ALE and GKM significantly affected the succession and assembly of soil bacterial communities at cross-regional scales. Specifically, the optimal environmental factors affecting bacterial Bray-Curtis similarity were elevation and temperature seasonality. The spatial factors and climate change impact on bacterial communities under the geographical barriers surpassed that of local soil microenvironments. The assembly pattern of bacterial communities transitions from local drift to cross-regional heterogeneous selection. Environmental factors had a relatively weak influence on myxomycetes and fungi. Both soil myxomycetes and fungi faced considerable dispersal limitation at local and cross-regional scales, ultimately leading to weak geographical distribution patterns.IMPORTANCEThe impact of environmental selection and dispersal on the soil microbial spatial distribution is a key concern in microbial biogeography, particularly in large-scale geographical patterns. However, our current understanding remains limited. Our study found that soil bacteria displayed a distinct cross-regional geographical distribution pattern, primarily influenced by environmental selection. Conversely, the cross-regional geographical distribution patterns of soil myxomycetes and fungi were relatively weak. Their composition exhibited a weak association with the environment at local and cross-regional scales, with assembly primarily driven by dispersal limitation., Competing Interests: The authors declare no conflict of interest.