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Are ChatGPT and large language models “the answer” to bringing us closer to systematic review automation?

In this commentary, we discuss ChatGPT and our perspectives on its utility to systematic reviews (SRs) through the appropriateness and applicability of its responses to SR related prompts. The advancement of artificial intelligence (AI)-assisted technologies leave many wondering about the current ca...

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Detalles Bibliográficos
Autores principales: Qureshi, Riaz, Shaughnessy, Daniel, Gill, Kayden A. R., Robinson, Karen A., Li, Tianjing, Agai, Eitan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148473/
https://www.ncbi.nlm.nih.gov/pubmed/37120563
http://dx.doi.org/10.1186/s13643-023-02243-z
Descripción
Sumario:In this commentary, we discuss ChatGPT and our perspectives on its utility to systematic reviews (SRs) through the appropriateness and applicability of its responses to SR related prompts. The advancement of artificial intelligence (AI)-assisted technologies leave many wondering about the current capabilities, limitations, and opportunities for integration AI into scientific endeavors. Large language models (LLM)—such as ChatGPT, designed by OpenAI—have recently gained widespread attention with their ability to respond to various prompts in a natural-sounding way. Systematic reviews (SRs) utilize secondary data and often require many months and substantial financial resources to complete, making them attractive grounds for developing AI-assistive technologies. On February 6, 2023, PICO Portal developers hosted a webinar to explore ChatGPT’s responses to tasks related to SR methodology. Our experience from exploring the responses of ChatGPT suggest that while ChatGPT and LLMs show some promise for aiding in SR-related tasks, the technology is in its infancy and needs much development for such applications. Furthermore, we advise that great caution should be taken by non-content experts in using these tools due to much of the output appearing, at a high level, to be valid, while much is erroneous and in need of active vetting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-023-02243-z.