The Cost Reduction of Artificial Intelligence-Based Healthcare for Clinical Drug Use : A Literature Review

Etika wahyu Vitasari; Firman Pribadi

doi:10.35451/n6amkf09

Authors

Etika wahyu Vitasari University of Muhammadiyah Yogyakarta
Firman Pribadi Master of Hospital Management, Universitas Muhammadiyah Yogyakarta

DOI:

https://doi.org/10.35451/n6amkf09

Keywords:

Artificial Intelligence, Cost reduction , Clinical drug use, Healthcare based, Literature review

Abstract

Artificial Intelligence (AI) evolves from experimental frameworks to essential clinical tools, it is crucial to assess its economic and clinical viability for drug use sustainability. This literature review examines the cost reduction and impact of AI in clinical drug management. In accordance with PRISMA 2020 guidelines, nine credible studies published between 2021 and 2026 were synthesized from databases including Pummeled, Scopus, and Google Scholar. The findings are organized into three key dimensions: medication safety and adherence, economic resource optimization, and human-AI synergy. The results indicate that medication adherence safety is demonstrated through the role of AI in detecting drug interactions (with ChatGPT-4.0), reducing the risk by 15.2% and improving therapy adherence, especially for TB treatment with AICure, and reducing the risk of hospitalization through clinical decision support. Economic resource optimization is reflected in a 17.3-17.4% reduction in treatment costs for 2,150 high-risk patients, along with the use of low-cost clinical inputs without compromising accuracy. Meanwhile, the synergy between humans and AI underscores the importance of transparent collaboration between healthcare professionals and technology to build trust, although gaps in understanding remain. Overall, AI integration contributes to safer, more efficient, and more collaborative healthcare. However, this review highlights knowledge gaps that may hinder the adoption of this technology. Findings indicate that the application of artificial intelligence significantly improves performance in the pharmaceutical industry while reducing costs, but its success depends on adapting the system to real-world constraints.

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