What do we need to build explainable AI systems for the medical domain?

Source: Dev.to

Hospitals are using AI more and more, but when a computer gives an answer, people want to know why. We need explainable systems so doctors and patients can feel safe. These systems should show how a decision was reached, not just give a score. That builds trust and helps clinicians check results quickly.

Current Applications

• AI helps read medical images.
• It finds patterns in genetic tests.
• It sorts medical notes.

Yet many tools act like closed boxes.

Why Explainability Matters

• Without simple explanations, doctors rely on guesses and patients become worried.
• Laws about data and privacy push for clear, traceable answers.
• Hospitals want tools that support, not replace, judgment.
• Transparent AI can improve patient safety by making errors easier to spot and fix.

Design Considerations

• Focus on clear outputs.
• Provide easy checks and simple ways to retrace a decision.
• Ensure explanations are understandable to both clinicians and patients.

Impact

People will use smart tools more when they understand them, and medicine will benefit when technology speaks plain words, not riddles.

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What do we need to build explainable AI systems for the medical domain?