A meta-analysis of over 9,500 patients in major prostate cancer trials, including the pivotal VISION and PSMA-4 trials for radioligand therapy, shows significant underrepresentation of Black and Hispanic patients. This creates a critical evidence gap when applying these therapies to diverse real-world populations.

Many genetic tests for personalized nutrition are validated on narrow populations, like European Caucasians. These genetic markers often have zero predictive power when applied to other ethnic groups, such as those of West African descent, making their recommendations highly unreliable for a diverse user base.

The burgeoning field of polygenic risk scores is dangerously unregulated, with some well-capitalized companies selling products that are 'no better than chance.' The key differentiator is rigorous, public validation of their predictive models, especially across ancestries, a step many firms skip.

DNA Complete's model of providing raw genomic risk scores tied to individual scientific papers, without context or curation, can be dangerously misleading. A user might see a low-risk result for a disease that is irrelevant to their ethnicity, highlighting the critical need for proper data interpretation in consumer health.

Despite billions invested over 20 years in targeted and genome-based therapies, the real-world benefit to cancer patients has been minimal, helping only a small fraction of the population. This highlights a profound gap and the urgent need for new paradigms like functional precision oncology.

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 FDA is requiring higher US patient enrollment in global trials to address concerns that results from predominantly non-US populations (e.g., Asia) may not be generalizable. This reflects worries about differences in prior standard-of-care treatments and potential pharmacogenomic variations affecting outcomes.

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.

Advanced health tech faces a fundamental problem: a lack of baseline data for what constitutes "optimal" health versus merely "not diseased." We can identify deficiencies but lack robust, ethnically diverse databases defining what "great" health looks like, creating a "North Star" problem for personalization algorithms.