Pioneering an AI clinical copilot with Penda Health

AI systems have the potential to improve human health globally—to make reliable health information universally available, help clinicians deliver better care, and empower people to better understand and advocate for their health. 

Large language model (LLM) performance and safety in health continue to advance. OpenAI model performance on HealthBench⁠ doubled from GPT‑4o to o3, and frontier models often outperform experts on tasks like diagnostic reasoning⁠(opens in a new window) and clinical summarization⁠(opens in a new window). Yet adoption towards solving real-world patient and clinician challenges remains slow. To realize the potential of LLMs in health, the ecosystem will need to close the model-implementation gap—the chasm between what models can do and how they are used in practice. 

To advance research on real-world implementation, OpenAI partnered with Penda Health⁠(opens in a new window), a primary care provider operating in Nairobi, Kenya since 2012, to conduct a novel study of Penda’s LLM-powered clinician copilot. Penda built their copilot, AI Consult, to provide clinicians with LLM-written recommendations at key points during a patient visit. AI Consult acts as a real-time safety net that activates only when there might be an error, keeping clinicians fully in control. 

In a study of 39,849 patient visits across 15 clinics, clinicians with AI Consult had a 16% relative reduction in diagnostic errors and a 13% reduction in treatment errors compared to those without. 

We believe this outcome was the result of three key factors:

Today, we are publishing the study findings alongside a closer look at Penda’s successful implementation, offering the ecosystem an early template for the safe and effective use of LLMs to support clinicians.