Project Details
Description
Background: Systematic literature reviews (SLRs) are products of high academic, methodological and care value within the biomedical sciences. They not only summarize knowledge, but also support decision making at different levels of knowledge application. There is a need to automate the selection of relevant references during their development. Although more than fifty articles have been found reporting an option to automate reference selection, the route for researchers to identify an optimal method that fits their needs is not clear. There is a need to harmonize the knowledge fields of informatics, data science, and biomedical sciences so that decision making is based on a common language. The objective of this research is to develop a methodological framework to support decision making on the use of digital tools employing artificial intelligence (HD-IA) in the automation of relevant reference selection during systematic reviews and to develop a HD-IA.
Methodology: It will be developed in three stages: I) Identify the evidence to automate the preliminary selection of references, II) generate the SELECTION-AI methodological framework and III) generate the SELECTION-DT tool. The methods to be used include scoping reviews, expert consensus, workflow adaptation based on the identification of input and output variables, Python development and testing, and two focus groups to describe the experience of using the generated products.
Expected results: To present information on the state of knowledge of tools to automate the preliminary selection of references in the development of systematic reviews and to provide a methodological instrument to support decision making regarding how to choose automation tools and how to evaluate them. In addition, it is expected to contribute with a tool that can be used in academic research contexts, health technology assessment, as well as in the development of clinical practice guidelines or public policies.
Methodology: It will be developed in three stages: I) Identify the evidence to automate the preliminary selection of references, II) generate the SELECTION-AI methodological framework and III) generate the SELECTION-DT tool. The methods to be used include scoping reviews, expert consensus, workflow adaptation based on the identification of input and output variables, Python development and testing, and two focus groups to describe the experience of using the generated products.
Expected results: To present information on the state of knowledge of tools to automate the preliminary selection of references in the development of systematic reviews and to provide a methodological instrument to support decision making regarding how to choose automation tools and how to evaluate them. In addition, it is expected to contribute with a tool that can be used in academic research contexts, health technology assessment, as well as in the development of clinical practice guidelines or public policies.
Keywords
Automation, Artificial intelligence, Screening, Reference screening, Systematic reviews, Validation, Methodological framework, Textual classifiers
Commitments / Obligations
Academic articles
Current evidence on automating reference scanning
Items to guide text classifier selection
Enriched list of items
SELECTION-AI framework appearance validation
Current evidence on automating reference scanning
Items to guide text classifier selection
Enriched list of items
SELECTION-AI framework appearance validation
| Status | Active |
|---|---|
| Effective start/end date | 3/15/25 → 8/31/26 |
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Main Funding Source
- National
Location
- Bogotá D.C.