Objective: Abstract screening is a labor-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies. We aimed to validate large language models (LLMs) used to automate abstract screening. Materials and Methods: LLMs (GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o, Llama 3 70B, Gemini 1.5 Pro, and Claude Sonnet 3.5) were trialed across 23 Cochrane Library systematic reviews to evaluate their accuracy in zero-shot binary classification for abstract screening. Initial evaluation on a balanced development dataset (n = 800) identified optimal prompting strategies, and the best performing LLM-prompt combinations were then validated on a comprehensive dataset of replicated search results (n = 119 695). Results: On the development dataset, LLMs exhibited superior performance to human researchers in terms of sensitivity (LLMmax = 1.000, humanmax = 0.775), precision (LLMmax = 0.927, humanmax = 0.911), and balanced accuracy (LLMmax = 0.904, humanmax = 0.865). When evaluated on the comprehensive dataset, the best performing LLM-prompt combinations exhibited consistent sensitivity (range 0.756-1.000) but diminished precision (range 0.004-0.096) due to class imbalance. In addition, 66 LLM-human and LLM-LLM ensembles exhibited perfect sensitivity with a maximal precision of 0.458 with the development dataset, decreasing to 0.1450 over the comprehensive dataset; but conferring workload reductions ranging between 37.55% and 99.11%. Discussion: Automated abstract screening can reduce the screening workload in systematic review while maintaining quality. Performance variation between reviews highlights the importance of domain-specific validation before autonomous deployment. LLM-human ensembles can achieve similar benefits while maintaining human oversight over all records. Conclusion: LLMs may reduce the human labor cost of systematic review with maintained or improved accuracy, thereby increasing the efficiency and quality of evidence synthesis.