Objective The purpose of this study is to systematically evaluate the efficacy of artificial intelligence (AI) tools in the review of dual-channel outpatient prescriptions for health insurance, and to explore the practical value of the human-machine collaborative review model. Method Retrospectively selected 138 Medicare dual-channel outpatient prescriptions in our hospital information system (HIS) from January to March 2025 as the study object and utilized four AI tools, namely, Tencent Yuanbao (deep seek), Doubao, Kimi Chat, and DeepSeek web version, to screen the prescriptions for standardization and medication appropriateness based on the Audit Specification and the Hospital Prescription Review Management Code (for Trial Implementation) to screen prescriptions for standardization and medication appropriateness, with manual review results as the gold standard. Comparison of timeliness, accuracy, and problem detection consistency of prescription reviews by 4 AI tools. Result The average review time consumed by Tencent Yuanbao (90±12 s), Doubao for (36±4 s), and Kimi Chat for (20±5 s) was significantly lower than that of manual review (276±23 s), with Kimi Chat being the fastest (P<0.05). Tencent Yuanbao (deep seek), Doubao and Kimi Chat had false-positive rates of 9.23%, 3.08% and 3.08%, respectively, and false-negative rates of 62.5%, 12.50% and 75%, respectively, with Doubao having the highest accuracy rate. Doubao (Deep Thinking) achieved the highest detection rate (62.50%), followed by Tencent Yuanbao (deep seek) with a detection rate of 25.00%, while Kimi Chat had the lowest detection rate at 12.50% (P<0.05). Conclusion AI tools in the medical insurance dual-channel external dispensing prescription audit timeliness is better than manual, beanbag consistency detection rate and accuracy rate is the highest, has a better application efficacy, human-computer synergy mode is worth paying attention to.