Aleksey V. Cherepov, Vice-Rector for Strategic Development
2/1, Bldg. 1, Barrikadnaya str., Moscow, 125993
Laila M. Malikova, Cand. of Sci. (Med.), Head of the International Affairs Department
2/1, Bldg. 1, Barrikadnaya str., Moscow, 125993
Mariya V. Makarovskaya, Cand. of Sci. (Med.), Associate Professor, Department of General Medical Practice and Outpatient Therapy, Russian Medical Academy of Continuous Professional Education of the Ministry of Healthcare of the Russian Federation; Functional Diagnostics Physician, State Budgetary Healthcare Institution Clinical Diagnostic Center No. 4 of the Moscow Department of Healthcare
3, Krylatskie Hills str., Moscow, 121609
Aleksey S. Ryazanov, Dr. of Sci. (Med.), Professor, Head of the Department of General Medical Practice and Outpatient Therapy, Russian Medical Academy of Continuous Professional Education of the Ministry of Health of the Russian Federation
2/1, Bldg. 1, Barrikadnaya str., Moscow, 125993
Artificial intelligence (AI) is increasingly used to support organizational and managerial processes in healthcare, although evidence of real-world effects remains heterogeneous.
Aim. To identify and critically evaluate the evidence on the impact of AI technologies on organizational processes in healthcare and to assess their potential applicability BRICS healthcare systems.
Methods. A systematic review with narrative followed PRISMA 2020. PubMed and eLibrary.ru (incorporating the Russian Science Citation Index) and CyberLeninka were searched between January and February 2026. Primary empirical and implementation studies, reviews, and selected methodological, governance-related, and contextual papers published between 2017 and 2026 were eligible. After multistage screening, 78 publications were included.
Results. The strongest evidence concerned predictive analytics for patient flow management and resource allocation, which reduced patient waiting times by 18–26% and achieving high predictive accuracy. Machine learning algorithms applied to operating room scheduling reduced idle time and improved resource utilization. Ambient and generative AI documentation tools were associated with reduced administrative workload and reduced clinician burnout. Evidence for revenue cycle management, conversational AI, logistics, and supply chain optimization was mainly based on model-development studies, reviews, or limited implementation reports. Several large-scale BRICS-related implementation studies came from Russia, while evidence from other BRICS healthcare systems was less consistently represented. Major barriers included fragmented infrastructure, limited interoperability, workforce gaps, and governance challenges.
Conclusion. AI may improve selected organizational processes, but long-term economic effectiveness, scalability, sustainability, and reproducibility require further evaluation in diverse healthcare settings.
The authors declare that there are no conflicts of interest present.