What does this research mean for the field?

While large language models alone are less accurate than human raters at appraising scientific evidence, human-AI collaboration can effectively reduce the workload for secondary human raters in assessing reporting and methodological rigor, though not for complex trial design tasks. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

September 12, 2024Open Access

Benchmarking Human–AI collaboration for common evidence appraisal tools

TWTim WoelfleUniversity of Basel JHJulian HirtUniversity of Bern PJPerrine JaniaudUniversity of Bern

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Abstract

BACKGROUND AND OBJECTIVE: It is unknown whether large language models (LLMs) may facilitate time- and resource-intensive text-related processes in evidence appraisal. The objective was to quantify the agreement of LLMs with human consensus in appraisal of scientific reporting (Preferred Reporting Items for Systematic reviews and Meta-Analyses PRISMA) and methodological rigor (A MeaSurement Tool to Assess systematic Reviews AMSTAR) of systematic reviews and design of clinical trials (PRagmatic Explanatory Continuum Indicator Summary 2 PRECIS-2) and to identify areas where collaboration between humans and artificial intelligence (AI) would outperform the traditional consensus process of human raters in efficiency. STUDY DESIGN AND SETTING: Five LLMs (Claude-3-Opus, Claude-2, GPT-4, GPT-3.5, Mixtral-8x22B) assessed 112 systematic reviews applying the PRISMA and AMSTAR criteria and 56 randomized controlled trials applying PRECIS-2. We quantified the agreement between human consensus and (1) individual human raters; (2) individual LLMs; (3) combined LLMs approach; (4) human-AI collaboration. Ratings were marked as deferred (undecided) in case of inconsistency between combined LLMs or between the human rater and the LLM. RESULTS: Individual human rater accuracy was 89% for PRISMA and AMSTAR, and 75% for PRECIS-2. Individual LLM accuracy was ranging from 63% (GPT-3.5) to 70% (Claude-3-Opus) for PRISMA, 53% (GPT-3.5) to 74% (Claude-3-Opus) for AMSTAR, and 38% (GPT-4) to 55% (GPT-3.5) for PRECIS-2. Combined LLM ratings led to accuracies of 75%-88% for PRISMA (4%-74% deferred), 74%-89% for AMSTAR (6%-84% deferred), and 64%-79% for PRECIS-2 (29%-88% deferred). Human-AI collaboration resulted in the best accuracies from 89% to 96% for PRISMA (25/35% deferred), 91%-95% for AMSTAR (27/30% deferred), and 80%-86% for PRECIS-2 (76/71% deferred). CONCLUSION: Current LLMs alone appraised evidence worse than humans. Human-AI collaboration may reduce workload for the second human rater for the assessment of reporting (PRISMA) and methodological rigor (AMSTAR) but not for complex tasks such as PRECIS-2.

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