Tag
This paper presents SRBench, a benchmark dataset for evaluating LLM performance in systematic review screening, and introduces PromptSR, a tool for managing prompt experiments and analysis.
Jev Reviewer is an open-source tool that assists systematic reviewers by using AI to extract direct quotes from research articles, supporting various file types and ensuring all processing occurs locally in the browser.
This paper provides a systematic review of AI and deep learning applications, specifically convolutional neural networks with data augmentation, for early lung cancer detection through image analysis. It analyzes 96 articles, highlighting both the potential and the challenges in clinical implementation.
Curie is an AI research assistant built for scientific literature analysis, featuring parallel search across multiple databases and claim verification against source texts.
This paper conducts a systematic review of AI for data center energy optimization, identifies research gaps, and proposes the CLEAR-DC framework to close the optimizer-load loop by integrating energy, carbon, and water metrics.
This paper presents a systematic review of software frameworks for explainable AI in time series classification, comparing their features, evaluation practices, and limitations.
This paper systematically reviews 55 studies on machine learning applications in autism spectrum disorder, focusing on diagnosis and treatment challenges and opportunities.
This systematic review examines the applications of large language models in mental health, covering innovations in areas like clinical conversational agents and multimodal learning, while highlighting ethical challenges and advocating for safe deployment frameworks.
This paper benchmarks auxiliary uncertainty signals from a BERT+GCN classifier to improve LLM-assisted systematic review screening, showing that targeted MAYBE-only routing maximizes efficiency with near-baseline cost.
A systematic review of XAI research in the context of the EU Right to Explanation, analyzing gaps between legal requirements and technical implementations across GDPR and the AI Act.
A systematic review of 257 open-source projects in the comments of the DeepSeek Harness beta recruitment post reveals a three-tier ecosystem, with DeepSeek support at 49%, and offers recommendations for beta testing priorities.
This paper compares expert-assigned and automatically-assigned MeSH terms as features for systematic review screening classifiers, showing that evaluation design significantly affects measured performance gaps, with canonical designs showing larger gaps that attenuate under alternative designs.
This systematic literature review investigates autonomous UAV route planning for coverage maximization in environmental monitoring, analyzing 562 records and reporting preliminary findings on algorithmic families, metrics, and validation practices.
This systematic review evaluates methodological reliability in machine learning models for early Chronic Kidney Disease prediction, revealing that data leakage inflates reported accuracy by over 15% and that more than 80% of predictors lack stability across studies.
A systematic review of non-social media free-text datasets for mental health disorder detection, identifying biases and gaps in current resources.
This paper evaluates the use of small language models (SLMs) to assist title and abstract screening in systematic reviews of social-physical human-robot interaction (spHRI). While SLMs did not match human performance, they operated locally at high speed and identified additional relevant papers, demonstrating their potential to augment human reviewers for large-scale literature reviews.
This paper presents a systematic review and benchmark of 24 black-box uncertainty estimation methods for large language models across 4 models and 4 dataset settings, finding that no single method dominates but hybrid methods that combine multiple uncertainty signals perform well.
A systematic review of machine learning and deep learning techniques for cattle identification, covering methods like CNNs and YOLO, feature extraction techniques, and challenges such as limited datasets and real-time processing.
This systematic review of 204 EduNLP papers reveals that teachers are underrepresented as beneficiaries despite being most affected, real-world deployment is rare, and ethical engagement tends toward acknowledgement rather than action, highlighting a tension between private-sector incentives and foundational educational needs.
This systematic scoping review examines three categories of large AI models in dental healthcare: language-generative models, discriminative vision foundation models, and dental-specific foundation models, analyzing 97 studies to show that general-purpose and domain-specific models play complementary roles, with integrated pipelines outperforming single-model approaches.