ArticleCureus2026
Prescription Analysis in the Digital Era: Comparing Artificial Intelligence-Based Versus Manual Approaches.
Article in Cureus, 2026. 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
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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
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Who cites it
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Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Prescription auditing using the World Health Organization (WHO) core prescribing indicators is a standard method to evaluate rational drug use. With advances in artificial intelligence (AI), AI-based tools may offer faster approaches for prescription analysis; however, comparative evidence with manual analysis remains limited. The objective of this study was to compare AI-based prescription analysis with manual prescription analysis using WHO core prescribing indicators. Methodology An observational study was conducted at a tertiary care teaching hospital over a period of three months. A total of 308 outpatient prescriptions were collected from the Medicine, Obstetrics and Gynecology, and Orthopedics outpatient departments. Prescriptions were analyzed manually according to WHO core prescribing indicators. AI-based analysis was performed using Google Gemini Pro (Google LLC, Mountain View, California). Results from both approaches were compared using a paired t-test and the chi-square test. Results AI-based prescription analysis produced results largely comparable to manual analysis for most WHO prescribing indicators. No significant differences were observed between AI and manual analysis for most prescribing indicators. However, a significant difference was noted in the percentage of medicines prescribed from the essential medicines list in the Medicine and Obstetrics and Gynecology departments. Conclusion AI-based prescription analysis demonstrated results broadly comparable to conventional manual analysis for most WHO core prescribing indicators. These findings suggest that AI-assisted tools may serve as a feasible supportive approach for prescription auditing.
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