IBM's Quantum AI for Precision Medicine Powered by GNQ Insilico

<ul>

<li>A curated, evidence-weighted biomedical causal knowledge graph</li>

<li>Multi-layer causal reasoning, moving from predictive modeling (what is likely to happen) to biologically grounded mechanistic reasoning (how and why it happens) to counterfactual exploration (what if a different intervention were made)</li>

<li>Executed through its Quantum Biological Reasoning Model (QBRM)</li>

</ul>

<p>This architecture enables interventional and counterfactual reasoning across a large-scale causal graph. At the core is a living, multimodal Patient 360 model (BioAvatar) that integrates:</p>

<ul>

<li>EHR</li>

<li>Multi-omics</li>

<li>Imaging</li>

<li>Digital pathology</li>

<li>Streaming data</li>

</ul>

<p>Insights are delivered through BioLens, providing explainable and traceable outputs for scientific and regulatory workflows.</p>

<p>Using this causal framework, GNQ enables:</p>

<ul>

<li>Mechanistic adverse event (AE) hypothesis generation with full causal evidence chains and source provenance</li>

<li>Early and low-frequency safety signal detection, improving lead time vs. traditional case accumulation (timing varies by dataset and implementation)</li>

<li>Individual patient risk stratification for toxicity prediction and contraindication profiling</li>

<li>Resistance trajectory forecasting to support proactive therapy optimization and stratified care</li>

<li>In silico trial design and cohort simulation, including eligibility matching to reduce protocol risk</li>

<li>Explainable, audit-ready narratives for regulatory and clinical workflows (e.g., IND/NDA, PSUR/PBRER, DSMB reviews, tumor boards)</li>

</ul>

<p>Built on AWS leveraging AWS HealthOmics, Amazon EKS, AWS Lambda, Amazon ECR, Amazon S3, Amazon DynamoDB, Amazon RDS (PostgreSQL), AWS Secrets Manager, AWS IAM, and Amazon Bedrock (on roadmap).</p>