Rather than relying on a small group of experts, OpenAI has built a three-tiered system involving over 260 physicians. This includes high-level strategic advisors, a large cohort for data operations like red-teaming and comparison tasks (communicating via Slack), and a core group of close advisors who translate this collective expertise into concrete evals and training data for researchers.

In a partnership with Kenya's Penda Health, OpenAI conducted the first randomized controlled trial of an LLM co-pilot for physicians. The study demonstrated a statistically significant improvement in diagnosis and treatment outcomes for patients whose doctors used the AI assistant. This provides crucial real-world evidence that AI can move beyond lab benchmarks to tangibly improve care.

Microsoft’s approach to superintelligence isn't a single, all-knowing AGI. Instead, the strategy is to develop hyper-competent AI in specific verticals like medicine. This deliberate narrowing of domain is not just a development strategy but a core safety principle to ensure control.

An effective AI strategy in healthcare is not limited to consumer-facing assistants. A critical focus is building tools to augment the clinicians themselves. An AI 'assistant' for doctors to surface information and guide decisions scales expertise and improves care quality from the inside out.

In a sign of recursive capability improvement, OpenAI found that its model-based grader for the HealthBench evaluation benchmark was more accurate and consistent than the average human physician performing the same grading task. This demonstrates that models can not only perform a task but also evaluate that performance at a superhuman level, a key component of scalable oversight.

An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.

Society holds AI in healthcare to a much higher standard than human practitioners, similar to the scrutiny faced by driverless cars. We demand AI be 10x better, not just marginally better, which slows adoption. This means AI will first roll out in controlled use cases or as a human-assisting tool, not for full autonomy.

Goodfire AI defines interpretability broadly, focusing on applying research to high-stakes production scenarios like healthcare. This strategy aims to bridge the gap between theoretical understanding and the practical, real-world application of AI models.

Dr. Jordan Schlain frames AI in healthcare as fundamentally different from typical tech development. The guiding principle must shift from Silicon Valley's "move fast and break things" to "move fast and not harm people." This is because healthcare is a "land of small errors and big consequences," requiring robust failure plans and accountability.