A recent Harvard Business Review article highlights a critical issue: generalist AI often struggles in healthcare because it misses context, nuance, and specialized knowledge. Models can read charts, but still misinterpret what key signals actually mean in practice. The takeaway is clear: AI doesn’t just need more data, it needs high-quality, validated, domain-aware data. Without strong data infrastructure, even powerful models can produce dangerous errors. This is where new infrastructure layers matter. Distributed ecosystems like Perceptron aim to support environments where data, models, and outputs can be continuously evaluated, validated, and improved. The future of AI won’t be determined by access to models alone, but by the quality of the data behind them and the systems used to verify them. 🔗Source: