Objective To compare the comprehension, readability, quality, safety, and trustworthiness of AI-assisted versus human-generated plain language summaries (PLSs) for Cochrane systematic reviews. Design Randomised, parallel-group, two-arm, non-inferiority trial (ISRCTN85699985). Setting Online survey platform, September 2025. Participants Adults aged 18 or older with a minimum English reading proficiency of 7 out of 10, recruited via Prolific. Of the 500 individuals screened, 465 were randomised and 453 completed per-protocol analysis. Interventions Participants were randomly assigned to three AI-assisted PLSs developed with ChatGPT and human-in-the-loop verification, or to three published human-generated Cochrane PLSs for the same reviews. Outcomes Primary: comprehension (10-item questionnaire, non-inferiority margin 10%). Secondary: readability quality and safety, trustworthiness, and authorship perception. Results Mean comprehension scores were 88.9% (n=228) in the AI-assisted group and 89.0% (n=225) in the human-generated group (mean difference -0.03 percentage points, 95% CI: -1.9% to 2.0%); the upper CI bound (2.0 percentage points) did not exceed the +10 percentage-point non-inferiority margin, demonstrating non-inferiority. Flesch-Kincaid Grade Level showed no significant difference (8.20 vs 8.38, p=0.722), although formal non-inferiority was missed (upper 95% CI bound 1.72 exceeded the 1.0 grade level margin). AI-assisted summaries scored higher on Flesch Reading Ease (63.33 vs 50.00, p=0.008) and lower on the Coleman-Liau Index. All summaries met pre-specified quality and safety standards (100% in both groups). Trustworthiness scores were comparable (3.98 vs 3.91, difference 0.068, 95% CI: -0.043 to 0.179; meeting non-inferiority). Participants demonstrated limited ability to distinguish between authorship, correctly identifying AI-assisted summaries in 56.3% of cases and human-generated summaries in 34.7% (≈ chance for a three-option question), with 55.4% of human-generated summaries misattributed as AI-assisted. Exploratory subgroup analysis showed an age interaction (p=0.023), though based on a small subgroup (n=14, 3%). Conclusions AI-assisted PLSs with human oversight achieved comprehension levels noninferior to those of human-generated Cochrane summaries, with comparable quality, safety, and trust ratings. AI summaries were largely indistinguishable from those generated by humans. Pre-trial verification identified and corrected numerical errors, confirming the need for human oversight. These findings support human-in-the-loop AI workflows for PLS production, though formal evaluation of the time and resource implications is needed to establish efficiency gains over traditional manual methods.