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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Abrantes, B. F., Miao, X., Trigo, V. & António, N. (2025). Competitiveness of artificial intelligence's (AI) technology-adoption in the healthcare sector in China: An expert analysis. Cureus Journal of Business and Economics. 2
Exportar Referência (IEEE) 
B. T. Abrantes et al.,  "Competitiveness of artificial intelligence's (AI) technology-adoption in the healthcare sector in China: An expert analysis", in Cureus Journal of Business and Economics, vol. 2, 2025
Exportar BibTeX 
@article{abrantes2025_1777286534847,
	author = "Abrantes, B. F. and Miao, X. and Trigo, V. and António, N.",
	title = "Competitiveness of artificial intelligence's (AI) technology-adoption in the healthcare sector in China: An expert analysis",
	journal = "Cureus Journal of Business and Economics",
	year = "2025",
	volume = "2",
	number = "",
	doi = "10.7759/s44404-025-08306-9",
	url = "https://www.cureusjournals.com/journal/business-and-economics"
}
Exportar RIS 
TY  - JOUR
TI  - Competitiveness of artificial intelligence's (AI) technology-adoption in the healthcare sector in China: An expert analysis
T2  - Cureus Journal of Business and Economics
VL  - 2
AU  - Abrantes, B. F.
AU  - Miao, X.
AU  - Trigo, V.
AU  - António, N.
PY  - 2025
SN  - 3059-202X
DO  - 10.7759/s44404-025-08306-9
UR  - https://www.cureusjournals.com/journal/business-and-economics
AB  - This research examined China’s three-tier national hospital system to understand how Artificial Intelligence (AI) technology influences unit-level competitiveness in both clerical and clinical functions. Through content analysis, the study found that the sector currently operates at the level of artificial narrow intelligence. Experts largely agreed that its adoption has been driven by the need to meet hospitals’ unitary requirements.
Fragmentation emerged as the dominant paradigm in experts’ perspectives, with four distinct clusters of opinion reflecting stakeholder-specific, compartmentalized views of AI’s utility. While sentiment was divided, it remained broadly positive, shaped by two key rupture factors: (i) the varying cultural and technical readiness across hospitals, and (ii) the paradoxical allocation of financial resources within the current national public funding framework-perceived as disproportionate to each unit’s specialization and thereby constraining further investment in AI initiatives.
ER  -