ArticleDigital health
Impact of artificial intelligence and digital upgrading on the sustainable development of public health services: An empirical study based on double machine learning.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: With the global proliferation of chronic diseases and sudden infectious outbreaks, the use of artificial intelligence and digital technologies to enhance the sustainable growth of public health services has become a key research focus. This study examines the impact and transmission mechanisms of artificial intelligence and digital upgrading on the long-term development of China's public health services. Methods: It visualizes and performs regression analysis on panel data from 30 provincial-level units in China from 2012 to 2024, using kernel density estimation, standard deviation ellipse, and dual machine learning models. Results: The following conclusions are drawn: (1) The sustainable development of public health services in China shows a regional distribution pattern, with higher levels in the east and lower levels in the west. Although overall levels have improved over time, regional disparities have widened. Hotspots for sustainable growth also show a spatial development trend toward the southeast. (2) Artificial intelligence and digital upgrading significantly positively impact the sustainable growth of public health services in China. A one-unit rise in artificial intelligence and digital enhancement results in gains of 0.014% and 0.080% in the sustainability of public health services, respectively. (3) The positive effects of artificial intelligence and digital upgrading on the sustainable development of public health services exhibit heterogeneity across economic zones, resource endowments, and the North-South regional division. (4) Digital upgrading and artificial intelligence significantly enhance the development of green technological innovation, green patent technology innovation, and green utility model innovation. Through this pathway, the sustainable development performance of public health services will be further improved. Conclusion: Digital upgrading and artificial intelligence improve the sustainable development of public health services in China through multiple pathways, including spatial distribution dynamics, direct positive effects, heterogeneous regional impacts, and enhanced green technological innovation.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.