Screenathon 2.0: human–AI collaborative screening applied to patient-generated health data

Abstract

Abstract Systematic reviews are essential for evidence-based research, yet the traditional screening process is time-consuming and difficult to scale. Human-only screening can introduce inconsistency, while fully automated approaches employing Large Language Models often lack the contextual judgement required for complex decisions. To address this, we introduce a crowd-based screening methodology that integrates human expertise with adaptive machine learning. The methods have been applied in the context of a large EU project where experts from 27 collaborating partners jointly screened 5842 papers across eleven disease topics related to patient-generated health data in a span of 2 days. Post-processing played a central role in ensuring data quality, including topic reallocation, targeted full-text verification, and noisy-label filtering. This Screenathon resulted in 487 records being labeled as relevant and 6,463 records as irrelevant. The number of records screened per participant ranged from 3 to 2496, with a mean of 216.4 records per screener ( SE  = 95.19). Exploratory analyses using survey results indicated increased trust in AI-assisted systematic reviewing after the event, along with generally positive evaluations of usability. The current Screenathon demonstrates that crowdsourced human–AI collaboration requires thoughtful training and calibration, together with strong post-processing safeguards.