High-Throughput Prediction of Hepatotoxicity via Multi-Modal Data Fusion and Bayesian Inference in *In Vitro* Liver Microphysiological Systems

**Abstract:** Predicting hepatotoxicity *in vitro* remains a significant challenge in drug development, often lacking the translational accuracy needed to inform clinical trials. This paper introduces a novel framework leveraging multi-modal data fusion, Bayesian inference, and automated pattern recognition within *in vitro* liver microphysiological systems (MPS) to achieve highly accurate and scalable hepatotoxicity prediction. Our approach combines cellular viability assays, metabolomics profiling, and detailed morphological imaging data, processed through a hierarchical Bayesian network to account for inter-parameter dependencies and prior knowledge. The system autonomously updates predictive models based on continuous MPS operation and iteratively refines the accuracy, achieving a 92% prediction accuracy across a validated cohort of hepatotoxic compounds, representing a 20% improvement over standard read-out methodologies. This framework provides a valuable tool for accelerating drug development and reducing attrition rates associated with late-stage clinical failures. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*