This session introduces the concept of Accurate Medicine, an evolution beyond Precision Medicine that integrates the complexities of patients, diseases, and real-world clinical practice. Rather than focusing solely on technology or genomics, this approach models the dynamic interrelationships among these three components to optimize therapeutic development and treatment outcomes. A panel of experts will demonstrate how innovative disease stratification and translational insights can enhance efficacy, personalize care, and maximize clinical benefit within modern healthcare systems.

A nonlinear dynamics and bifurcation-based framework for modeling complex medical systems connects mechanistic theory with modern machine learning. This approach enhances interpretability and prediction across multimodal data streams. A software package is presented that enables practical, scalable, and MLOps-ready implementation of these methods in biomedical data science.

In the first part of the presentation, we outline the significance of a transdisciplinary approach to understanding of neurodegenerative mechanisms—from quantum scales to virtual humans—as the foundation for integrating insights across biological, computational, and physical domains. Building on this framework, we introduce a modular, quantum-computing–first pipeline that operationalizes population-level neural state-space modeling. The approach integrates ELA-secure preprocessing, population-aware detrending, multi-subject alignment, and dimensionality reduction to ensure data integrity and scalability. It advances interpretability through Ising/PMEM inference combined with energy-landscape and phase-diagram analytics, enabling the extraction of kinetic descriptors and facilitating meaningful cross-subject comparability in neurodegenerative research.

This talk will present a data automation strategy developed in our discovery group. The fully integrated platform covers end-to-end workflows for key biologics discovery assays, streamlining data capture and processing from lab instruments to an analysis-ready database for both transactional and analytical needs. The platform reduced data processing turnaround from hours to seconds, enabling rapid, data-driven decision-making in biologics R&D.

Reverse translation (aka bedside to bench) projects have been a rapidly growing focus area for target identification, patient stratification, and many other areas. Clinical studies are a valuable source of data and insights for reverse translational studies. We will illustrate the unexpected challenges and findings from locating, extracting, and using clinical studies using on two case studies.

Pharmacokinetic (PK) modeling is widely used in drug development, yet its impact is often limited by inconsistent integration into decision workflows. This case study presents a production-ready data science framework that operationalizes PK-driven predictive models alongside Intuence Discovery to support compound selection and portfolio decisions. By embedding validated models into cross-functional workflows, teams improved predictability, reduced late-stage attrition, and accelerated decision-making across discovery and development.

We present a privacy-preserving approach using federated averaging to train convolutional neural networks on small-scale molecular datasets where data cannot be moved or shared. By simulating a multi-center environment with publicly available phenotype-linked omics data, we evaluate how varying site participation and model reuse affect performance—demonstrating that federated learning can improve generalizability and robustness even in low-data, distributed biomedical research settings.

AI, genomics, and multimodal data science are reshaping rare-disease diagnosis and dramatically reducing the diagnostic odyssey. This closing joint session brings together leaders in precision medicine, bioinformatics, national rare-disease infrastructure, and real-world patient advocacy to highlight breakthrough models for rapid genomic interpretation, data integration, and clinical deployment. Attendees will gain a unified, cross-disciplinary view of what’s required to deliver faster, more accurate, and more equitable rare-disease diagnoses.