Lauri Diehl

Wednesday 7th October 2026

Panel Discussion: Beyond the Slide: Tissue AI as the Anchor for Multimodal Biomarker Discovery & Global Deployment

11:20 am

One Frontier Model, For Every Stage of Drug Development: Discover how a unified ecosystem powered by a single frontier AI model streamlines biomarker discovery, clinical trial stratification, regulatory-grade diagnostics, and global deployment to pathology labs. Platform agnostic development under one roof – no need for algorithm hand-offs or tedious integrations before deployment
De-Risking CDx Timelines With Tissue AI: AI-driven biomarker signal detection reduces trial enrollment risk and enables more informed decisions from less tissue — shortening Rx-Dx co-development time
Redefining Global Pathology Routine: Moving tissue biomarkers from isolated reference labs to global clinical routine powered by our extensive CRO partnerships and an active lab network

Wednesday 7th October 2026

Panel Discussion: Beyond IHC Scoring: Unlocking Next-Generation Clinical Biomarkers through AI-Powered Computational Pathology

2:10 pm

Tumor heterogeneity determines treatment response and resistance, and AI-powered computational pathology makes heterogeneity tractable for drug development
Tissue analysis succeeds in drug development when scientific, computational, and delivery expertise are integrated from the start, not assembled project by project
Connecting imaging features to outcomes to generate predictive biomarkers, and translation into cross-cohort and cross-platform validation and deployment
Infrastructure and global access shape which biomarkers can actually reach patients. H&E-based approaches can have an important role in ensuring equitable, global CDx access

Wednesday 7th October 2026

Promise & Pitfalls of Tumor Target Quantification in Antibody-Drug Conjugate & Immune Cell Engager Drug Development

2:40 pm

Target expression in indication selection and as potential predictive biomarkers
Digital pathology approaches and technical challenges
Optimizing program investment and deciding what data will be enough