As AI handles complex diagnoses and treatment data, the doctor's primary role will shift to the 'biopsychosocial' aspects of care—navigating family dynamics, patient psychology, and social support for life-and-death decisions that AI cannot replicate.

To ensure reliability in healthcare, ZocDoc doesn't give LLMs free rein. It wraps them in a hybrid system where traditional, deterministic code orchestrates the AI's tasks, sets firm boundaries, and knows when to hand off to a human, preventing the 'praying for the best' approach common with direct LLM use.

Despite hype in areas like self-driving cars and medical diagnosis, AI has not replaced expert human judgment. Its most successful application is as a powerful assistant that augments human experts, who still make the final, critical decisions. This is a key distinction for scoping AI products.

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.

A key risk for AI in healthcare is its tendency to present information with unwarranted certainty, like an "overconfident intern who doesn't know what they don't know." To be safe, these systems must display "calibrated uncertainty," show their sources, and have clear accountability frameworks for when they are inevitably wrong.

To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.

The most effective use of AI isn't full automation, but "hybrid intelligence." This framework ensures humans always remain central to the decision-making process, with AI serving in a complementary, supporting role to augment human intuition and strategy.

Dr. Fei-Fei Li asserts that trust in the AI age remains a fundamentally human responsibility that operates on individual, community, and societal levels. It's not a technical feature to be coded but a social norm to be established. Entrepreneurs must build products and companies where human agency is the source of trust from day one.

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.