#systematic-review News & Analysis

8 articles tagged with #systematic-review. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

8 articles

AINeutralarXiv – CS AI · May 97/10

🧠

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code

A systematic review of 114 studies reveals that code quality defects in large language models stem primarily from training data imperfections rather than model limitations alone. The research establishes a taxonomy linking 18 propagation mechanisms between data quality issues and generated code failures, while advocating for proactive data governance over reactive post-generation filtering.

AINeutralarXiv – CS AI · Jun 86/10

🧠

AI-Driven Test Case Generation from Natural Language Requirements: A Survey of Techniques and Research Gaps

A comprehensive survey of AI and NLP techniques for automating test case generation from natural language requirements identifies 21 primary studies across three evolutionary eras. The research reveals that no existing approach fully addresses six critical quality dimensions—automation, ambiguity handling, domain applicability, traceability, evaluation thoroughness, and hallucination control—highlighting significant gaps in current software testing automation.

AINeutralarXiv – CS AI · Jun 56/10

🧠

Where's the Structure? A Systematic Literature Review of Empirical Research on Human-AI Collaboration and Hybrid Intelligence for Learning

A systematic literature review of 62 empirical studies examines human-AI collaboration in educational settings, finding that unstructured interaction between humans and AI produces suboptimal learning outcomes. The research identifies key design principles and structural frameworks that educational technologists can apply to create more effective AI-enhanced learning systems.

AINeutralarXiv – CS AI · Jun 26/10

🧠

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Systematic Review and Practical Design Guidelines

A systematic review of self-supervised learning (SSL) in medical imaging analyzes 75 studies to establish that SSL effectiveness depends on alignment between pretext task design, imaging modality, and clinical objectives. The research provides practical guidelines showing contrastive methods excel at classification while generative approaches better support segmentation, with no universal optimal strategy.

AINeutralarXiv – CS AI · May 276/10

🧠

Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches

A comprehensive systematic review of 139 studies reveals that multimodal information fusion improves document classification accuracy by 5.28 percentage points, while multiview approaches provide modest gains of 4.67%. The research identifies critical gaps in methodological rigor, with less than 24% of studies employing statistical validation, highlighting the need for more robust research standards in the field.

AIBullisharXiv – CS AI · Apr 136/10

🧠

TiAb Review Plugin: A Browser-Based Tool for AI-Assisted Title and Abstract Screening

Researchers developed TiAb Review Plugin, an open-source Chrome extension that enables AI-assisted screening of academic titles and abstracts without requiring server subscriptions or coding skills. The tool combines Google Sheets for collaboration, Google's Gemini API for LLM-based screening, and an in-browser machine learning algorithm achieving 94-100% recall, demonstrating practical viability for systematic literature reviews.

🧠 Gemini

AINeutralarXiv – CS AI · Mar 276/10

🧠

Factors Influencing the Quality of AI-Generated Code: A Synthesis of Empirical Evidence

A systematic literature review of 24 studies reveals that AI-generated code quality depends on multiple factors including prompt design, task specification, and developer expertise. The research shows variable outcomes for code correctness, security, and maintainability, indicating that AI-assisted development requires careful human oversight and validation.

AINeutralarXiv – CS AI · Mar 36/103

🧠

Toward Youth-Centered Privacy-by-Design in Smart Devices: A Systematic Review

A systematic review of 122 academic papers reveals significant gaps in privacy protection for youth using AI-enabled smart devices, with technical solutions dominating research (67%) while policy enforcement and educational integration remain underdeveloped. The study recommends a multi-stakeholder approach involving policymakers, manufacturers, and educators to create comprehensive privacy ecosystems for young users.