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.

Beyond early discovery, LLMs deliver significant value in clinical trials. They accelerate timelines by automating months of post-trial documentation work. More strategically, they can improve trial success rates by analyzing genomic data to identify patient populations with a higher likelihood of responding to a treatment.

AI's most significant impact won't be on broad population health management, but as a diagnostic and decision-support assistant for physicians. By analyzing an individual patient's risks and co-morbidities, AI can empower doctors to make better, earlier diagnoses, addressing the core problem of physicians lacking time for deep patient analysis.

The next evolution in personalized medicine will be interoperability between personal and clinical AIs. A patient's AI, rich with daily context, will interface with their doctor's AI, trained on clinical data, to create a shared understanding before the human consultation begins.

OpenAI's health division serves a dual purpose: delivering societal benefits and providing a real-world, high-stakes environment for AI safety research. Problems like scalable oversight (supervising superhuman AI) move from theoretical exercises to practical necessities when models outperform physicians on narrow tasks, creating concrete feedback loops that accelerate safety progress.

The conversation around AI in healthcare often focuses on patient-facing chatbots. However, the more significant, unspoken trend is adoption by clinicians themselves. As of last year, two out of three American doctors were already using AI for administrative tasks, translation, and even as a 'wingman' for clinical diagnosis.

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.

The widespread use of AI for health queries is set to change doctor visits. Patients will increasingly arrive with AI-generated analyses of their lab results and symptoms, turning appointments into a three-way consultation between the patient, the doctor, and the AI's findings, potentially improving diagnostic efficiency.

By continuously feeding lab results and treatment updates into GPT-5 Pro, the speaker created an AI companion to validate the medical team's decisions. This not only caught minor discrepancies but, more importantly, provided immense peace of mind that the care being administered was indeed state-of-the-art.