Background: Server-based screening tools impose subscription costs, while open-source alternatives require coding skills. Objectives: We developed a browser extension that provides no-code, serverless artificial intelligence (AI)-assisted title and abstract screening and examined its functionality. Methods: TiAb Review Plugin is an open-source Chrome browser extension (available at https://chromewebstore.google.com/detail/tiab-review-plugin/alejlnlfflogpnabpbplmnojgoeeabij). It uses Google Sheets as a shared database, requiring no dedicated server and enabling multi-reviewer collaboration. Users supply their own Gemini API key, stored locally and encrypted. The tool offers three screening modes: manual review, large language model (LLM) batch screening, and machine learning (ML) active learning. For ML evaluation, we re-implemented the default ASReview active learning algorithm (TF-IDF with Naive Bayes) in TypeScript to enable in-browser execution, and verified equivalence against the original Python implementation using 10-fold cross-validation on six datasets. For LLM evaluation, we compared 16 parameter configurations across two model families on a benchmark dataset, then validated the optimal configuration (Gemini 3.0 Flash, low thinking budget, TopP=0.95) with a sensitivity-oriented prompt on five public datasets (1,038 to 5,628 records, 0.5 to 2.0 percent prevalence). Results: The TypeScript classifier produced top-100 rankings 100 percent identical to the original ASReview across all six datasets. For LLM screening, recall was 94 to 100 percent with precision of 2 to 15 percent, and Work Saved over Sampling at 95 percent recall (WSS@95) ranged from 48.7 to 87.3 percent. Conclusions: We developed a functional browser extension that integrates LLM screening and ML active learning into a no-code, serverless environment, ready for practical use in systematic review screening.

翻译：背景：基于服务器的筛选工具需支付订阅费用，而开源替代方案要求编码技能。目的：我们开发了一款浏览器扩展，提供无代码、无需服务器的人工智能（AI）辅助标题与摘要筛选功能，并验证其性能。方法：TiAb Review Plugin是一款开源Chrome浏览器扩展（下载地址：https://chromewebstore.google.com/detail/tiab-review-plugin/alejlnlfflogpnabpbplmnojgoeeabij）。该工具采用Google Sheets作为共享数据库，无需专用服务器，支持多审阅者协作。用户需自行提供Gemini API密钥，密钥经本地加密存储。工具提供三种筛选模式：人工审阅、大语言模型（LLM）批量筛选及机器学习（ML）主动学习。ML评估方面，我们以TypeScript重写了ASReview默认主动学习算法（TF-IDF结合朴素贝叶斯）以实现浏览器内运行，并通过六组数据集的十折交叉验证确认其与原始Python实现等效。LLM评估方面，我们在基准数据集上对比了两个模型家族的16种参数配置，最终选定最佳配置（Gemini 3.0 Flash，低思考预算，TopP=0.95），基于敏感度优化的提示词在五个公开数据集（记录数1038至5628条，发生率0.5%至2.0%）上进行验证。结果：TypeScript分类器在全部六组数据集上的前100位排序结果与原始ASReview完全一致。LLM筛选的召回率达94%至100%（精确率2%至15%），95%召回率下的采样节省工作量（WSS@95）范围为48.7%至87.3%。结论：我们开发的浏览器扩展功能完备，将LLM筛选与ML主动学习整合至无代码、无服务器环境，可实际应用于系统性综述筛选工作。