From the course: AI in Healthcare: Transforming Bedside Outcomes
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Challenges in achieving interpretability
From the course: AI in Healthcare: Transforming Bedside Outcomes
Challenges in achieving interpretability
- While we've talked through how machine learning and AI are transforming healthcare, clinicians need to trust these tools in order for them to be effective. This trust comes from interpretability, which means the ability to understand how an AI model makes its decisions. While interpretability is crucial, it's not always easy to achieve. So let's explore three challenges that stand in the way and why they matter. Firstly, complexity of advanced models. AI models, especially deep learning algorithms, are incredibly powerful, but with that power comes great complexity. These models often involve millions or even billions of parameters, making it hard to explain why they make specific predictions. For example, a model might predict that a patient has a high risk of heart failure but without knowing which factors, like blood pressure or lab results or medical histories, had actually contributed to that prediction. So, clinicians may actually hesitate to act on it. If we can't explain how…
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