Numenos AI found that unifying biological data without traditional borders, such as incorporating mouse data or cancer data for dermatological diseases, surprisingly increases the predictive accuracy of their models. This challenges the siloed approach to traditional research.

Long before the current hype, a 2005 AI project used electronic medical records and genomic data to find children with an ultra-rare disease who would have otherwise died before age 10. This highlights AI's long-standing, life-saving impact beyond recent commercial applications.

AI platforms can analyze existing medical images, like CT scans ordered for a cough, to find subtle, early signs of cancers. This repurposes vast amounts of routine diagnostic data into a powerful, passive screening tool, allowing for incidental discoveries of diseases like pancreatic cancer without new procedures.

The progress of AI in predicting cancer treatment is stalled not by algorithms, but by the data used to train them. Relying solely on static genetic data is insufficient. The critical missing piece is functional, contextual data showing how patient cells actually respond to drugs.

The next frontier in preclinical research involves feeding multi-omics and spatial data from complex 3D cell models into AI algorithms. This synergy will enable a crucial shift from merely observing biological phenomena to accurately predicting therapeutic outcomes and patient responses.

The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.

The low-hanging fruit of finding a single predictive biomarker is gone. The next frontier for bioinformatics is developing complex, 'multimodal models' that integrate several data points to predict outcomes. The key challenge is creating sophisticated models that still yield practical, broadly applicable clinical insights.

Frontier AI models excel in medicine less because of their encyclopedic knowledge and more because of their ability to integrate huge amounts of context. They can synthesize a patient's entire medical history with the latest research—a task difficult for any single human. This highlights that the key to unlocking AI's value is feeding it comprehensive data, as context is the primary driver of superhuman performance.

Myome and Natera are building foundational models for oncology that function like genomic language models. By training on vast cancer sequence and clinical data, these models learn the context of a patient's disease to predict the next mutation, similar to how transformers like GPT predict the next word in a sentence.