Generative artificial intelligence use in evidence synthesis: A systematic review DOI
Justin Clark, Belinda Barton, Loai Albarqouni

et al.

Research Synthesis Methods, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: April 24, 2025

Abstract Introduction With the increasing accessibility of tools such as ChatGPT, Copilot, DeepSeek, Dall-E, and Gemini, generative artificial intelligence (GenAI) has been poised a potential, research timesaving tool, especially for synthesising evidence. Our objective was to determine whether GenAI can assist with evidence synthesis by assessing its performance using accuracy, error rates, time savings compared traditional expert-driven approach. Methods To systematically review evidence, we searched five databases on 17 January 2025, synthesised outcomes reporting or taken, appraised risk-of-bias modified version QUADAS-2. Results We identified 3,071 unique records, 19 which were included in our review. Most studies had high unclear Domain 1A: selection, 2A: conduct, 1B: applicability results. When used (1) searching missed 68% 96% (median = 91%) studies, (2) screening made incorrect inclusion decisions ranging from 0% 29% 10%); exclusion 1% 83% 28%), (3) data extractions 4% 31% 14%), (4) assessments 10% 56% 27%). Conclusion shows that current does not support use without human involvement oversight. However, most tasks other than searching, may have role assisting humans synthesis.

Language: Английский

Generative artificial intelligence use in evidence synthesis: A systematic review DOI
Justin Clark, Belinda Barton, Loai Albarqouni

et al.

Research Synthesis Methods, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: April 24, 2025

Abstract Introduction With the increasing accessibility of tools such as ChatGPT, Copilot, DeepSeek, Dall-E, and Gemini, generative artificial intelligence (GenAI) has been poised a potential, research timesaving tool, especially for synthesising evidence. Our objective was to determine whether GenAI can assist with evidence synthesis by assessing its performance using accuracy, error rates, time savings compared traditional expert-driven approach. Methods To systematically review evidence, we searched five databases on 17 January 2025, synthesised outcomes reporting or taken, appraised risk-of-bias modified version QUADAS-2. Results We identified 3,071 unique records, 19 which were included in our review. Most studies had high unclear Domain 1A: selection, 2A: conduct, 1B: applicability results. When used (1) searching missed 68% 96% (median = 91%) studies, (2) screening made incorrect inclusion decisions ranging from 0% 29% 10%); exclusion 1% 83% 28%), (3) data extractions 4% 31% 14%), (4) assessments 10% 56% 27%). Conclusion shows that current does not support use without human involvement oversight. However, most tasks other than searching, may have role assisting humans synthesis.

Language: Английский

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