Abstract

This presentation discusses the role of artificial intelligence (AI) in enhancing the literature review process and its potential to transform scientific knowledge acquisition. The presentation highlights the importance of literature review in research and the challenges associated with the traditional manual approach. The presentation emphasizes that integrating AI in literature review can significantly improve efficiency, accuracy, and reduce bias. AI-powered tools can automate various aspects of the literature review process, including search, selection, analysis, and synthesis of relevant literature. The benefits of AI in literature review include increased efficiency, improved coverage of literature, and the ability to identify gaps in knowledge and uncover new research questions.The presentation also provides a comprehensive list of AI tools that can be used in literature review, such as Cramly.ai, Quillbot, GPT-minus 1, ChatGPT, Samwell.ai, and many others. These tools offer functionalities such as rewriting, paraphrasing, summarizing, understanding literature, and extracting key information from articles.The future of AI in literature review is promising, with emerging trends such as deep learning models and knowledge graphs. These trends have the potential to enhance the accuracy and comprehensiveness of literature reviews. In conclusion, the integration of AI in literature review has the potential to revolutionize scientific knowledge acquisition by improving efficiency, accuracy, and coverage of literature. By combining AI with human expertise, researchers can unlock new insights and accelerate scientific progress in various fields.

Keywords: AI Tools, Artificial Intelligence (AI), Literature Review, Research Process, Scientific Knowledge Acquisition

Introduction

• Artificial Intelligence (AI) is a branch of computer science that focuses on

  creating intelligent machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

• Literature review (LR) is a crucial component of the research process.

• LR involves examining and analysing previously published works related to

  a particular research topic or question.

• The aim of a LR is to identify gaps in knowledge, establish the current state

  of research in a particular field, and provide a foundation for future research.

• Integrating AI in literature review can significantly enhance the process and potentially revolutionize scientific knowledge acquisition. 

• AI can automate the search and analysis of literature, resulting in improved efficiency, increased accuracy, and reduced bias.

• The significance of integrating AI in literature review cannot be overstated as it has the potential to change the landscape of research and discovery.

Literature Review Process

• The traditional LR process involves a manual search and analysis of relevant published works. 

• It typically involves selecting databases, identifying keywords, and manually reviewing search results to identify relevant literature. 

• The selected literature is then read and analysed to extract relevant information, which is then synthesised into a coherent narrative.

• The process is time-consuming and subject to human bias, resulting in incomplete searches and inadequate coverage of literature.

• The volume of literature available can make it difficult for researchers to keep up with the latest research findings. 

• These challenges highlight the need for a more efficient and effective way of conducting literature reviews, which is where AI comes in.

How AI can Enhance Literature Review

AI can automate various aspects of the LR process, including search, selection, analysis, and synthesis of relevant literature. 

• AI-powered tools can automatically search and analyse vast amounts of literature in a matter of minutes, identifying relevant publications based on a given set of criteria, such as keywords or specific journals.

The Benefits of AI in Literature Review

• Increased efficiency and accuracy. 

• Reduced human bias.

• Improved coverage of literature. 

• With AI, researchers can conduct more comprehensive and up-to-date LRs, enabling them to identify gaps in knowledge and uncover new research questions. 

• AI can help researchers to extract key information from articles and synthesise it into meaningful insights, facilitating the process of developing new hypotheses and theories.

Types of AI used in Literature Review

• Cramly.ai: This is a rewriting AI tool.

• Quillbot: This is an AI tool for Paraphrasing. Copy, Paste and click Rephrase.

• Spinbot: This is just like Quillbot, i.e. a Paraphraser.

• GPT-minus 1: This is a tool for Paraphrasing. Copy, Paste and Paraphrase with Synonyms.

• ChatGPT: This is an assistive AI tool for academic work.

• Samwell.ai: This is an assistive AI tool for writing academic papers.

• Explain paper: This is an AI tool for understanding literature. Highlight a particular word or phrase in the paper and it will explain it fully.

• Paper digest: This is an AI tool for LR. It summarizes papers in few seconds. Click the DOI of the paper and click to Digest.

• Chatdoc.com: This tool is used for LR. Type in your question based on the paper uploaded and it does the rest for you.

• Humata.ai: Ask question based on your paper and the AI tool will summarize the paper for you.

• Semantic scholar: This is an AI tool for understanding a paper at a glance. It extracts meaning and identifies connections from within papers.

• Connected papers: This is a unique, visual tool to support researchers find and explore papers relevant to their field of interest, for creating bibliography, discovering most relevant papers, and making sure an important paper is not missed.

• Research rabbit.ai: It works like Connected papers. It links papers.

• Dimensions ai: This is an AI tool that summarises paper.

• Chisquares.com: This is a powerful AI tool that effortlessly collects and analyses data. It could be used for collaborative researches.

• Elicit.com: This is an AI tool for full LR because it summarises and has a lot of columns for reviewing papers.

• Scholarcy.com: This is an Article summariser and Flashcard generator. 

•  Scispace ai: This is used for Paraphrasing, LR, and Summarising paper.

• Scite.ai: This AI tool is used for summarising research papers, in-text citations, etc.

• Wordtune read.ai: This requires you to upload your pdf paper and it will do the summary. 

• Litmaps.com: This is an AI tool used for searching relevant papers for LR. It could be used to find research gaps in various papers.

• Open Knowledge maps.org: Get an overview of a research topic, find Open Access papers, identify relevant concepts, and separate the wheat from the chaff, using this AI tool.

• Citefast: This is an AI tool used for Citation and Referencing. Copy the title of the paper and paste it to search.

• MyBib: This AI tool is also used for referencing. Copy the title of the paper and paste to search, save and download.

Future of AI in Literature Review

• The future of AI in LR is promising, with several emerging trends that have the potential to transform the way researchers acquire knowledge. 

• One such trend is the use of deep learning models for LR tasks, such as text classification and sentiment analysis. 

• These models can extract meaning from text more accurately and efficiently than traditional methods, enabling researchers to conduct more comprehensive and precise LRs.

• Another emerging trend is the use of knowledge graphs, which are structured representations of knowledge that enable researchers to explore relationships between concepts and identify new research opportunities. 

• Knowledge graphs are particularly useful for LR tasks that require researchers to synthesise information from multiple sources and identify patterns in the data.

• While AI has the potential to significantly enhance the LR process, it is unlikely to replace traditional review processes entirely. 

• Rather, AI is more likely to be used in conjunction with human expertise, with researchers using AI tools to automate routine tasks and free up time for more complex analysis and synthesis.

  Addendum: Scholarcy - Read Smarter

  

  Accessing Scholarcy

  

  Scholarcy Research Paper Summarizer Features

• Summarisation settings: This sets the length of the Scholarcy summary section of the flashcard. You can choose to have a summary with a fixed word limit set by the words option, or as a fraction of the original document set by the % option.

• Summary engine: This can be set to articles, which gives a good result for a broad range of documents, or books/chapters, which tends to work better for book chapters and longer texts.

• Rewrite in 3rd person: This rewrites the Scholarcy summary section into a neutral third person (for example ‘The authors’ results suggest …’) to make it easier to quote from and reference sections of the paper.

• Structured summary: When checked, the AI will aim to structure the summary according to the structured of the source document, for example Introduction, Objectives, Methods, Results, Conclusion.

• Section snippets: When checked, snippets from each section will be shown in the flashcard, and the AI will sample from these snippets when Structured summary is also enabled. When unchecked, the full content of each section will be shown and used as the input to the summary engine.

• Auto highlighting: Facts (blue) and claims (beige) within the article are automatically highlighted for you, but you can uncheck this here to turn this feature off.

• Wikipedia links: This automatically link key terms throughout the text to their Wikipedia articles, but if this is getting in the way, you can switch this off.

• Extract tables: This identifies tabular data and their captions, showing the captions in a Tables section along with a download button to save the tables to an Excel file on your computer.

• Extract figures: This extracts figures and their captions into a Figures section. You can then click on each figure to show a larger version in a new browser tab. Each Figure will be linked to callouts in the text. Figure extraction is very CPU intensive and, for large documents with high-resolution images, may not always be successful, so it is disabled by default.

  Using Scholarcy (https://www.scholarcy.com)

  

  
  Using Scholarcy (https://article-summarizer.scholarcy.com)

  

  
  Using Scholarcy (https://article-summarizer.scholarcy.com)

  

  
  
  Scholarcy Summary Flashcard Features

• Compact summary

• Expanded summary

• Key concepts

• Abstract

• Synopsis

• Scholarcy highlights

• Scholarcy summary

• Comparative analysis

  Key Concepts

  

  
  Abstract

  

  
  Synopsis

  

  
  

  Scholarcy Highlights

  

  
  Scholarcy Summary

  

  
  Comparative Analysis

  

  
  
  
  Saving Your Documents on Scholarcy: Export (Word/PPT) or Save to Library

  

  Addendum: The Use of SciSpace AI in Literature Review (https://scispace.com)
  

  

Hopmepage of SciSpace (https://scispace.com)

Features of SciSpace AI

• Search for papers.

• Upload PDF for review.

• Create Library for your papers.

• Review Literature in less than 60 seconds without stress of reading.

• Plagiarism-free Literature review.

Reviewing Paper on SciSpace AI

The Reviewing Page of SciSpace AI

Features of SciSpace AI: Results

Features of SciSpace AI: Methods Used

Features of SciSpace AI: Literature Survey

Features of SciSpace AI: Explain Abstract

Features of SciSpace AI: Future Works

Features of SciSpace AI: Practical Implications

Features of SciSpace AI: Limitations

Referencing on SciSpace AI

Referencing on SciSpace AI

Conclusion

• AI has the potential to revolutionise scientific knowledge acquisition by enabling researchers to conduct more efficient, comprehensive, and accurate LRs. 

• The benefits of AI in LR include increased efficiency, accuracy, and coverage of literature, while the challenges include issues related to bias and quality control.

• Despite these challenges, by automating routine tasks and enabling researchers to synthesise information more efficiently, AI can help accelerate scientific discovery and facilitate the development of new hypotheses and theories.

• By combining the power of AI with human expertise, we can unlock new insights and accelerate scientific progress in a wide range of fields.

References

• Ananiadou, S. & McNaught, J. (2018). Text mining for biology and biomedicine. Artech House.

• Chen, X., Yang, M., Li, C., & Li, M. (2019). An overview of natural language processing for academic libraries. Journal of Academic Librarianship, 45(5), 471-478.

• El-Beltagy, S. R., Arafa, Y., & Rafea, A. (2020). Deep learning for text classification: A comprehensive review. Journal of Classification, 37(1), 97-133.

• Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1-12.

• Liao, H., Yang, L., Yu, Q., & Li, X. (2018). A survey of deep learning techniques for big data processing. Information Fusion, 42, 146-157.

• Liu, X., Liu, Y., Song, R., & Yin, X. (2021). A hybrid recommendation approach based on deep learning and text mining for academic papers. Journal of Intelligent & Fuzzy Systems, 40(5), 9701-9713.

• Oussalah, M. & Sekkai, F. (2019). Literature review using machine learning: A bibliometric analysis. Journal of Intelligent & Fuzzy Systems, 36(6), 6345-6356.

• Park, Y. W. & Chen, Y. (2020). Machine learning applications for biomedical literature review. Briefings in Bioinformatics, 21(5), 1678-1688.

• Raghupathi, W. & Raghupathi, V. (2018). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 6(1), 3.

• Warren-Jones, E., & Owango, J. (2023). Using Scholarcy in improving researcher’s academic writing and research output. AfriArXiv. https://doi.org/10.21428/3b2160cd.cf5aa77f

• Zhang, X., Chen, J., & Li, Y. (2020). Deep learning for academic literature review: An overview. Journal of Intelligent & Fuzzy Systems, 39(5), 6945-6954.