Artificial intelligence for literature reviews: opportunities and challenges

Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)

Опубликована: Авг. 17, 2024

Abstract This paper presents a comprehensive review of the use Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is rigorous and organised methodology that assesses integrates prior research on given topic. Numerous tools have been developed to assist partially automate process. The increasing role AI this field shows great potential providing more effective support for researchers, moving towards semi-automatic creation literature reviews. Our study focuses how techniques are applied semi-automation SLRs, specifically screening extraction phases. We examine 21 leading using framework combines 23 traditional features with 11 features. also analyse recent leverage large language models searching assisting academic writing. Finally, discusses current trends field, outlines key challenges, suggests directions future research. highlight three primary challenges: integrating advanced solutions, such as knowledge graphs, improving usability, developing standardised evaluation framework. propose best practices ensure robust evaluations terms performance, transparency. Overall, offers detailed overview AI-enhanced researchers practitioners, foundation development next-generation solutions field.

Язык: Английский

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Collaborative large language models for automated data extraction in living systematic reviews

Journal of the American Medical Informatics Association, Год журнала: 2025, Номер unknown

Опубликована: Янв. 21, 2025

Abstract Objective Data extraction from the published literature is most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data workflow leveraging large language models (LLMs) that mimics real-world 2-reviewer process. Materials and Methods A dataset of 10 trials (22 publications) LSR was used, focusing on 23 variables related trial, population, outcomes data. The split into prompt development (n = 5) held-out test sets 17). GPT-4-turbo Claude-3-Opus were used for extraction. Responses 2 LLMs considered concordant if they same given variable. discordant responses each LLM provided other cross-critique. Accuracy, ie, total number correct divided by responses, computed assess performance. Results In set, 110 (96%) concordant, achieving an accuracy 0.99 against gold standard. 342 (87%) concordant. 0.94. 0.41 0.50 Claude-3-Opus. Of 49 25 (51%) became after cross-critique, increasing 0.76. Discussion Concordant are likely be accurate. instances cross-critique can further increase accuracy. Conclusion Large models, when simulated collaborative, workflow, extract with reasonable performance, enabling truly “living” reviews.

Язык: Английский

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A homogeneous multimodality sentence representation for relation extraction

Information Fusion, Год журнала: 2025, Номер unknown, С. 102968 - 102968

Опубликована: Фев. 1, 2025

Язык: Английский

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Leveraging large language models for Human-Machine collaborative troubleshooting of complex industrial equipment faults

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103235 - 103235

Опубликована: Март 10, 2025

Язык: Английский

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Named entity recognition for construction documents based on fine-tuning of large language models with low-quality datasets

Automation in Construction, Год журнала: 2025, Номер 174, С. 106151 - 106151

Опубликована: Март 31, 2025

Язык: Английский

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A discrete convolutional network for entity relation extraction

Neural Networks, Год журнала: 2025, Номер 184, С. 107117 - 107117

Опубликована: Янв. 6, 2025

Язык: Английский

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Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 628 - 628

Опубликована: Янв. 10, 2025

In China, fruit tree diseases are a significant threat to the development of industry, and knowledge about is most needed professional for farmers other practitioners in industry. Research papers primary sources that represent cutting-edge progress disease research. Traditional engineering methods acquisition require extensive cumbersome preparatory work, they demand high level background information technology skills from handlers. This paper, perspective industry dissemination, aims at users such as farmers, experts, communicators, gatherers. It proposes fast, cost-effective, low-technical-barrier method extracting research paper abstracts—K-Extract, based on large language models (LLMs) prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs automate extraction knowledge. The has constructed comprehensive classification system and, through series optimized questions, effectively overcomes deficiencies LLM providing factual accuracy. tests multiple available Chinese market, results show can seamlessly integrate with any model, DeepSeek model Kimi performing particularly well. experimental indicate have accuracy rate handling judgment tasks simple Q&A tasks. simple, efficient, accurate, serve convenient tool agricultural field.

Язык: Английский

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OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System

Опубликована: Май 8, 2025

Язык: Английский

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Event extraction as machine reading comprehension with question-context bridging

Knowledge-Based Systems, Год журнала: 2024, Номер 299, С. 112041 - 112041

Опубликована: Июнь 5, 2024

Язык: Английский

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Adaptive feature extraction for entity relation extraction

Computer Speech & Language, Год журнала: 2024, Номер 89, С. 101712 - 101712

Опубликована: Авг. 13, 2024

Язык: Английский

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