05/27/2026
We’re not quite ready to fully rely on AI tools—especially EKG-based diagnostics—without stronger validation.
There’s still a lot of work to be done. One major gap is transparency and accessibility. Open-source models would allow clinical teams to test performance in their own environments, under real-world conditions—not just rely on published metrics.
AI is only as good as the data it’s trained on. If bias exists in the dataset, the model will learn and replicate it. That’s why building diverse, representative datasets is critical from the start.
Equally important is validating these models across different facilities and populations. Only then can we identify blind spots, improve reliability, and ensure these tools truly generalize in healthcare settings.
AI has promise—but rigor, testing, and inclusivity must come first.