A significant part of Unlearn.ai's value is not just its advanced generative models, but its painstaking data harmonization work. The company builds internal machine learning tools to unify complex, disparate data sources like clinical trials and real-world data, which is the essential foundation for creating powerful models.

Electronic Health Record (EHR) companies have historically used proprietary formats to lock in customers. AI's ability to read and translate unstructured data from any source effectively breaks these data silos, finally making patient data truly portable.

We possess millions of data points on interventions, but they are useless to AI models because they're trapped in thousands of disparate EMRs in varied formats. The challenge is not generating more data, but solving the human incentive and alignment problems required to create unified data registries.

Despite possessing one of the world's best clinical genomic databases, Memorial Sloan Kettering (MSK) recognized its limitations and partnered with Sophia Genetics. This highlights that collective intelligence from a federated network is essential, as even the most advanced single center cannot capture the full spectrum of patient diversity.

Contrary to the belief that AI requires perfect, clean data, the biggest opportunity lies in building technology that can find signals in messy, diverse data sets across different modalities and organisms. The tech should solve the data problem, not wait for it to be solved.

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.

Jurgi Camblong cautions against the hype that Large Language Models (LLMs) can solve every problem in medicine. Sophia Genetics uses a diverse "toolbox" of AI—including statistical methods and machine learning—selecting the most efficient mathematical model for a specific biological problem and dataset.

The primary challenge holding back precision medicine is not a lack of data or innovation. Instead, it's the operational difficulty of integrating and interpreting complex, siloed information quickly enough to make it clinically actionable for individual patients. The focus must shift from accumulation to execution.

Instead of costly proprietary data generation, Turbine focused on the 'unsexy' work of combining many different public and partner datasets. This capital-efficient approach forced them to build an AI model architected for generalization and data efficiency from the very beginning.