The Plural Eyes system significantly outperformed the standard double-reading protocol.

Background: Human error in radiological interpretation remains a significant contributor to diagnostic discrepancies. While Computer-Aided Diagnosis (CAD) has improved detection rates, current systems often suffer from high false-positive rates. This study evaluates "Plural Eyes," a novel multi-perspective algorithmic fusion system designed to simulate the consensus of multiple independent observers. Methods: We conducted a prospective, blinded, randomized controlled trial comparing standard dual-consultant review against the Plural Eyes AI-assisted review. The trial involved 2,400 radiological datasets (CT and MRI) from three tertiary care centers. The primary endpoint was diagnostic accuracy, measured by sensitivity and specificity against a reference standard of clinical and pathological follow-up. Results: The Plural Eyes arm demonstrated a sensitivity of 94.2% (95% CI, 92.1–96.3) compared to 87.5% (95% CI, 84.8–90.2) in the standard review arm ($p < 0.001$). Specificity improved from 82.4% to 89.6% ($p = 0.03$). The system reduced average interpretation time per case by 18%. Conclusion: The Plural Eyes system offers a statistically significant improvement in diagnostic accuracy and efficiency, suggesting that algorithmic simulation of multi-observer consensus is a viable strategy for clinical implementation.

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The mean interpretation time per case was reduced in the Plural Eyes arm (186 seconds vs. 227 seconds in control). Radiologists reported that the "Consensus Overlay" allowed for faster localization, effectively reducing the search time by approximately 15–20%.

The improvement in specificity is particularly noteworthy. Traditional CAD systems often flag benign anatomical variants as suspicious, leading to unnecessary follow-up procedures. Plural Eyes mitigates this by requiring "consensus" among its internal virtual observers. If three out of five internal algorithms do not agree on a lesion, the system suppresses the alert, mirroring the decision-making process of a tumor board.

This paper presents findings from the , a multi-center study testing a novel software architecture that synthesizes "multiple virtual perspectives" of a single dataset. Unlike standard AI models that output a single prediction, Plural Eyes utilizes an ensemble of distinct, trained neural networks to simulate a "committee of experts," fusing their outputs to achieve a consensus diagnosis.

The was diagnostic accuracy (sensitivity and specificity) verified against a composite reference standard of pathology reports or 6-month clinical follow-up. Secondary outcomes included mean interpretation time and the rate of false-positive recalls.

Participants were randomized 1:1 into two arms: