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Abstract With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review manageable. Though several notable advancements have already been made, the incorporation of machine learning is less than optimal, still relying on a sequential, staged process designed to accommodate a purely human approach, exemplified by PRISMA. Here, we test a spiral, alternating or oscillating approach, where full-text...
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Automatic synthesis of RCTs
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This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to this topic were collected from three major publication databases Google Scholar, Pubmed, and Scopus. ChatGPT models were used to...
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Abstract Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic...
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This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of...
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Academic databases are a fundamental source for identifying relevant literature in a field of study. Scopus contains more than 90 million records and indexes around 12,000 documents per day. However, this context and the cumulative nature of science itself make it difficult to selectively identify information. In addition, academic database search tools are not very intuitive, and require an iterative and relatively slow process of searching and evaluation. In response to these...
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Greetings, if our institution opts for the Tier 3 plan for the ChatGPT API, allowing students to utilize our API, and the usage either surpasses or falls below the predefined limits of Tier 3, how does it influence the monthly pricing? To clarify, if usage exceeds the Tier 3 limits, will our monthly payment remain fixed at $1000, or will the pricing be adapted according to our actual usage? You can refer to the link provided for details on the tier plans: https://platform.openai.com/docs/guides/...