Addressing Algorithmic
Bias in Healthcare
AI Health understands that bias is baked into the current healthcare system. In order to ensure more inclusive, patient-centric solutions for diverse populations, best practices in addressing these biases are employed.

Quality Assurance with Transparency
AI is typically seen as magic black boxes that arrive at a result with no explanation of how. AI Health uses explainable AI that “shows its work” to medical advisory members for transparency and constant quality assurance of the AI models.
Medical Advisory Oversight
AI Health’s medical advisory team identifies meaningful variables per use case within chronic illness to help mitigate bias in training datasets — like blood type, age, gender, skin color etc.
Constantly Pursuing Equitable Outcomes
The AI Health Platform uses AI models that constantly work to identify bias and continuously advance more equitable outcomes using feedback from medical advisory and patient outcomes.

Ethical Approach to AI
Quality Assurance through Explainable AI
AI is typically seen as magic black boxes that arrive at a result with no explanation of how. AI Health uses explainable AI that “shows its work” to medical advisory members for transparency and constant quality assurance of the AI models.