Our Approach to Building Safe GenAI for Healthcare

At MedAsk, we are developing a specialized second layer on top of a foundation model to deliver safe and effective health information. Our platform is designed to work with any foundation model, allowing us to select and upgrade the underlying AI based on performance rather than being locked into a specific technology. Our approach includes:

• Fine-Tuning on Proprietary Medical Data: Training the foundation model with data specific to the target use case ensures contextually appropriate responses.
• Integrating a Medical Knowledge Base: A structured, data-driven knowledge base grounds the model’s reasoning and responses in validated medical information.
• Rigorous Benchmarking: We continuously evaluate each iteration of MedAsk for diagnostic accuracy (correctly identifying likely conditions), triage accuracy (appropriately determining urgency levels), and medical coding accuracy (properly mapping conditions to standardized medical codes).
• Application logic: Custom prompting and control mechanisms facilitate appropriate user interactions and establish a robust decision-making process within MedAsk.
• Moderation and Safeguards: Automatic flagging systems for both inputs and outputs help identify and prevent potentially harmful or inappropriate content.
• Continuous Monitoring and Transparency: We collect and analyze user feedback to improve our tool. At the same time, we maintain clear disclaimers about its capabilities and limitations.
• Medical Expert Oversight: Our development team includes healthcare professionals who guide our system’s design, validate medical content, and ensure clinical accuracy in our processes.