ASReview markets itself as an AI-powered active learning tool to streamline systematic review screening. However, the reality reveals significant shortcomings that undermine its practical utility.

• The machine learning models are fragile and domain-dependent, often requiring extensive tuning and user expertise to avoid poor performance.
• It frequently suffers from data sparsity and cold-start problems, where insufficient initial training data leads to unreliable prioritization.
• The promise of drastically reducing screening workload is often overstated, with real-world time savings being marginal for many topics.

• ASReview's user interface is minimalistic but non-intuitive, demanding steep learning curves for new users.
• It operates largely as a standalone tool, lacking seamless integration with popular reference managers, systematic review platforms, or collaboration tools.
• Export and import functionalities are limited, complicating workflow continuity and reproducibility.

• The AI decision-making process is largely a black box, offering little explainability on why studies are prioritized or excluded.
• There are minimal options for user intervention or manual override of AI decisions without disrupting the learning process.
• This opacity raises concerns about bias, errors, and accountability in critical review stages.

• ASReview focuses mainly on title and abstract screening, neglecting later review stages such as data extraction or risk of bias assessment.
• It is less effective for reviews with highly heterogeneous studies, non-English literature, or niche topics with sparse data.
• The tool does not yet support multi-user collaboration natively, restricting its use in team settings.

• Being a research-driven open-source project, ASReview suffers from infrequent updates and variable documentation quality.
• User support channels are limited, placing the burden on individual teams to troubleshoot and customize.

ASReview offers an intriguing glimpse into AI-assisted review but remains an immature, niche tool with significant limitations in usability, transparency, and real-world effectiveness. Its deployment should be cautious and supplementary, not foundational.

Recommendation: Use ASReview only as an experimental adjunct to established review processes, not as a replacement for rigorous human screening and judgment.

• ✅ Intuitive, widely adopted systematic review platform
• ✅ AI-assisted screening suggestions integrated into workflows
• ✅ Strong collaboration, version control, and audit trails
• ✅ Integrates well with reference managers and export tools
• ➕ Why better than ASReview:

Robust workflow support combined with user-friendly AI assistance

• ✅ Advanced machine learning and text mining for screening prioritization
• ✅ Supports multiple review stages including bias assessment and data extraction
• ✅ Comprehensive workflow integration and audit features
• ➕ Why better than ASReview:

More mature AI features integrated within full systematic review platform

• ✅ Automated risk of bias assessment complementing screening
• ✅ Provides explanations for bias judgments improving transparency
• ✅ Can be integrated into review workflows for enhanced efficiency
• ➕ Why better than ASReview:

Extends automation beyond screening into critical appraisal stages

• ✅ User-friendly screening tool with AI suggestions and conflict resolution
• ✅ Supports team collaboration and manual screening alongside AI
• ✅ Free, web-based, accessible interface
• ➕ Why better than ASReview:

Balanced AI assistance with ease of use and accessibility

• Use Covidence for streamlined, team-based AI-assisted screening and review management.
• Use EPPI-Reviewer if you require mature AI features integrated into comprehensive review workflows.
• Use RobotReviewer to augment screening with automated risk of bias assessment.
• Use Rayyan for accessible, collaborative screening with helpful AI suggestions.
• Use ASReview primarily for experimental or niche AI-active learning projects.