Speaker
Description
Background:
A substantial proportion of clinical research waste originates from fundamental methodological flaws—improper study design, insufficient power, inappropriate statistical methods, and non-compliance with reporting guidelines. While many AI tools attempt to support data analysis, none address the critical upstream phase: validating methodology before data collection. To address this gap, we developed E-PICOS, an AI-assisted framework that ensures methodological rigor at the earliest stages of a clinical trial.
Methods:
E-PICOS integrates three intelligent components:
(1) Protocol Validation AI, which evaluates PICOS structure, identifies risks of selection bias, assesses sample frame adequacy, and recommends appropriate trial designs and estimands;
(2) Statistical Guidance Engine, which supports sample size calculation based on the Minimum Clinically Important Difference (MCID), ensuring clinical—not only statistical—meaningfulness;
(3) Reporting Optimization AI, which checks trial protocols and manuscripts for compliance with CONSORT, SPIRIT, ICH E6(R3), and estimand-based reporting frameworks.
Importantly, E-PICOS does not perform statistical computation itself; analyses (e.g., t-tests, ANOVA, ROC curves, Kaplan–Meier, Cox regression) are executed using established statistical engines, preserving validity and reproducibility. The AI interprets results in accordance with Good Biostatistical Practices and supports transparent reporting.
Results:
Across multiple real-world implementations, E-PICOS identified methodological issues at the protocol stage—including underpowered designs, inappropriate estimands, insufficient justification of effect sizes, and missing bias-mitigation strategies. Early correction of these issues improved protocol quality, reduced anticipated research waste, and enhanced compliance with international trial standards. E-PICOS also supported manuscript preparation by detecting major/minor deficiencies and generating structured compliance reports without storing user data.
Conclusion:
E-PICOS represents a novel paradigm in clinical trial methodology: AI-assisted validation before data collection. By combining methodological expertise, MCID-based power planning, and guideline-driven oversight of reporting, E-PICOS enhances rigor, transparency, and reproducibility while maintaining full data sovereignty. This framework offers a scalable and ethical model for improving clinical trial quality in the era of data-intensive research.
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