ai for literature review

ai for literature review

The Role of AI in Literature Review

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1. Introduction

Artificial intelligence (AI) is in the process of transforming the overall structural aspects of society. The field of research is at its highest peak, and consumers are likely to experience more benefits in the near future. AI is especially expected to automate and augment the sophisticated tasks of experts by creating learning and dialogue systems. Not only pinpointing the scientific and engineering research experts, AI is anticipated to undertake and profoundly perform the tasks of experts in various fields, including teaching, complex analysis, and decision making. The cost-effectiveness and performance of AI systems in the near future are likely to encourage businesses and industries to develop expert systems or hire the expertise of AI to undertake those complex tasks which were available only at the human expert level. With the rapid level of progress and growing interest in AI technologies, there lies a potential to perform a game-changing role and overcome certain limitations of traditional methods for the sake of betterment in every field. One of the promising applications of AI is the use of machine learning and deep learning to make sense of large datasets and automate various levels of data analysis. AI is expected to automate and augment human effort in large-scale data analysis of any domain through the development of machine learning and deep learning systems.

2. Benefits of AI in Literature Review

AI has numerous benefits for structuring and executing literature reviews, especially given its reliance on comprehensively searching large volumes of literature. For example, the conceptual and managerial nature of literature reviews in fields like medical research can benefit from AI-based information extraction techniques [5]. Here, cognitive tools can be used to search the medical literature, classify its content, and summarize information on, for example, the state of the science in a particular area. This would help researchers to keep abreast of current knowledge in their field and facilitate the identification of topic areas in need of further exploration [6]. AI also has applications at the more tactical and technical levels of conducting a review. For instance, one basic yet time-consuming task in preparing a literature review is to create a comprehensive bibliography. AI tools such as citation indexes can help researchers establish the impact of a particular author or article and also assist in identifying relevant material to include in their own written references [7]. At a nuts and bolts level, AI has a role in using word processing and referencing software to automate the construction and formatting of citations and references in the review, potentially saving hours of a researcher’s time. Another significant benefit of AI in literature review is its potential to eliminate the problem of information overload. This problem is generally caused by the vast number of papers published in a specific area and the inherent difficulties in keeping track of literature search results and any relevant new publications over a long period of time. Information overload can lead to researchers spending excessive time sifting through irrelevant information and result in them missing important findings or reading duplicate copies of work they have already seen. AI techniques such as automation and organization of literature searches, taxonomies, and keyword cross-referencing can help to create systems that find and index only the most relevant information for a review. An example is the Cochrane Collaboration, a global independent network of researchers, professionals, patients, careers, and people interested in health, which is renowned for rigorous reviews of healthcare interventions. Compared to a traditional Cochrane Review, one being planned with the use of automation tools, it was estimated that the inclusion of automation would save over 50% of time and effort in preparing the review and reduce information overload for the reviewers [8]. The ambitious goal of AI research in this area is to eventually produce search tools so intelligent that they give a precise answer to a researcher’s question, without them having to sift through and critically appraise the findings of several studies. Such tools, if accurate, would be very attractive to evidence-based practitioners.

3. Challenges of AI in Literature Review

Although the ability to understand document content is crucial, automated summarization and document categorization systems are at a rudimentary stage of development and do not always perform acceptably. Interactive tools that enable the user to provide immense knowledge in refining the system-generated results are generally more satisfactory. An example of a result-refining tool is UMLS-CUIS, a system to allow clinicians to select groups of MEDLINE documents relevant to their specific clinical interests, then receive automatic updates of new articles that are pertinent to the predefined search. These tools are often ignored by AI development teams as they are difficult to develop and require a high level of understanding from the user. Despite this, it is important that the capabilities of such systems are indeed realized, and future research must concentrate on this.

Deductive approaches to NLP may provide a solution to the problem of needing to understand the context of a set of documents prior to defining a query. Automatically generated, hierarchical categorization schemas that classify documents in a manner that facilitates generalized query formulation and execution are very useful. An example schema is the MeSH classification schema used by the National Library of Medicine. A system to classify MEDLINE documents according to MeSH categories has been developed and is now in use at the National Library of Medicine to assist in document indexing.

4. Implementing AI in Literature Review

A popular definition of AI (Artificial Intelligence) is “the study and design of intelligent agents”, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. Typical examples of AI which may be relevant to literature review are intelligent agents that categorize and filter emails and documents; information retrieval systems, such as web search engines, that use intelligent agents to better categorize information and to help the user find what he/she is looking for; and finally, information extraction from various sources including the web. A recent state-of-the-art article in this field is “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data” by J. Lafferty, A. McCallum, and F.C.N. Pereira. In this article, the authors describe a method for applying CRFs to build models for annotating sequences. This is a method that, if further developed, could provide substantial help to those undertaking a review in a complex area. An example would be the bioinformatics field, where there are many molecular sequence databases in genetics and protein structure; CRFs could be used to automate tagging complex sequences of data for easy interpretation. This is a similar technology to that used by speech recognition software, and if applied in this niche, could help a reviewer to sift and categorize through masses of data in a particular area.

5. Conclusion

The principal concern about using AI to process textual information is that automation changes the nature of the task. A common argument is that we should simplify and standardize research to create information that is suitable for input to computers, rather than using technology to handle existing complex and disparate information more efficiently. This is at odds with the idea that good quality information can empower people to make better-informed decisions, and with AI literature review an untested trade-off between efficiency and information quality.

For AI technology to be regarded as successful, it must replicate these judgments and actions and produce an identical or comparable result to that of a human expert. In some areas of review, it is plausible that AI may be able to accomplish this, but any attempt to automate the process of study identification, for example, risks generating comprehensive results with little distinction between studies of high and low relevance. Even if a particular AI application represents an improvement over existing methods, the opportunity cost of losing human reviewer expertise and the investments made in training human reviewers must be taken into account.

We have defined review as a methodologically complex activity. The objective of a review is to provide an exhaustive and up-to-date summary of the extant evidence bearing on a particular question. Undertaking a review is an exercise in applied science as much as an exercise in information retrieval; it requires a clear understanding of the question to be addressed, a lucid conceptual framework, rigorous attention to the identification, selection, and appraisal of primary research, and sophisticated methods to summarize and synthesize data. In this context, the primary tasks of the reviewer are to make a series of judgments and to employ skills and expertise specific to the given area of inquiry.

Throughout this review paper, we have explored the role of AI in literature review, using this as a touchstone issue to highlight both the potential and the limitations of AI in a specific domain. It is unsurprising that machine learning algorithms can be applied to the literature review task to successfully automate certain components of the selection, appraisal, and extraction processes. What is less certain is the degree to which such automation represents progress.

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