Explainable AI tools in this category help organisations understand, interpret, and communicate how AI models produce their outputs. These AI solutions in healthcare typically use methods such as feature attribution, model visualisation, counterfactual analysis, and traceable decision logic to reveal the factors that influence a prediction, recommendation, or classification. Key evaluation angles include the clinical and scientific validity of explanations, usability for intended users, model-performance trade-offs, auditability and documentation, data governance, and alignment with applicable regulatory and organisational requirements.

Browse the AI tools below to identify the Explainable AI solutions that best match your model-development, clinical, research, and governance requirements.

This category page is for informational purposes only and does not constitute regulatory, clinical, or investment advice; organisations should conduct their own technical, legal, and governance due diligence before selecting any AI solutions in healthcare.