The AI Model Produces a Clinical Output
Placeholder: patient features flow into a trained model.
- 6: Input features used (placeholder feature set)
- Risk Score: Output type (placeholder output form)
- Adjustable: Model class (placeholder complexity range)
- <100ms: Inference time (placeholder latency figure)
What the model sees
Placeholder text describing feature ingestion, short.
From features to score
Placeholder text describing forward pass, short summary.
Black-Box Scenario — Output With No Reasoning
Placeholder: the score arrives with no supporting detail.
- None: Explanation provided (placeholder black-box state)
- Low: Clinician confidence (placeholder trust level)
- Limited: Error detection ability (placeholder review capacity)
- Reduced: Regulatory acceptance (placeholder compliance note)
Why opacity is risky
Placeholder text on black-box clinical risk, short.
Trust without evidence
Placeholder text on unverifiable outputs, brief note.
Explainable-AI Techniques Reveal Contributing Factors
Placeholder: attribution method scores each input feature.
- Feature Attribution: Method type (placeholder method name)
- Up to 6: Features ranked (placeholder ranked count)
- Bar breakdown: Output form (placeholder visualization type)
- Minor: Added latency (placeholder compute cost)
How attribution works
Placeholder text on scoring feature influence, short.
Reading the bars
Placeholder text on bar magnitude meaning, brief.
Clinician Sanity-Checks the Explanation
Placeholder: clinician compares reasoning to clinical knowledge.
- Plausibility Check: Review step (placeholder review type)
- Variable: Flag rate (placeholder flag frequency)
- Advisory: Decision support role (placeholder role note)
- Retained: Override capability (placeholder clinician authority)
Sanity-checking reasoning
Placeholder text on plausibility review, short summary.
Catching model errors
Placeholder text on error-catching benefit, brief.
Trust Improves, but Complexity Costs Explainability
Placeholder: complex models trade clarity for raw accuracy.
- Positive: Trust gain (placeholder outcome direction)
- Improved: Error catch rate (placeholder benefit metric)
- Inherent Tradeoff: Accuracy vs. clarity (placeholder tension note)
- Harder to Explain: High-complexity models (placeholder deep-model note)
The inherent tradeoff
Placeholder text on complexity vs explainability tension.
Placeholder: deep models gain accuracy, lose clean explainability.
Balancing the two
Placeholder text on choosing model class by context.