AI Explainability is now available in the Piction Health app

What is Explainable AI (XAI)?

The ubiquity of AI models in almost every technology we interact with on a daily basis brings up an important need to understand how the AI model makes decisions. This need is heightened when it comes to AI models operating on medical imaging data and integrated in clinical decision support tools that advise doctors on the next course of action for a patient. It is both useful and important to uncover the black box of AI decision making to build trust between the doctor and the AI tool, and help doctors be confident in their decision making.

The challenge

When primary care physicians evaluate a skin condition, they aren’t sure of all the conditions a patient could have. When a dermatologist evaluates a skin condition, they usually build out a list of all the conditions that look similar. Then they go through them one by one to rule them as less likely or more likely based on their experience and medical history of the patient.

How we solved the challenge

Our AI models have been trained using the semantic segmentation approach to identify the segments or group of pixels within the image that were used by the AI model to come up with a list of visually similar conditions. Highlighting these pixels not only provides confidence to physicians about the outcome, but also provides them with valuable hints to focus on the right area of the rash.

What’s next?

AI Explainability is still a developing concept for AI models used in medical imaging. Regulatory bodies are also weighing in on this issue and inviting opinions from industry experts in order to come up with a regulatory framework for software enabled medical devices using AI. We will continue to monitor this space and will continue to improve on our transparency interface. We are just getting started.

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