During my recent work on privacy-preserving federated learning for healthcare, I reached a frustrating realization: high accuracy numbers don't always translate to real-world clinical trust.
The Gap Between Metrics and Understanding
Metrics like accuracy, F1-score, or AUC are easy to report. But they offer little insight into whether a clinician can actually rely on the model's recommendations. Knowing that the model predicts "dengue likely" 91% of the time doesn't tell the doctor why.
Why This Matters in Practice
In federated learning, privacy guarantees are strong, but they create barriers to transparency. Clinicians can't inspect patient-level contributions, and existing explainable AI tools only partially illuminate model behavior.
Bridging the Gap
Addressing this requires more than better metrics. We must design systems that respect both technical and epistemic constraints:
- Integrate interpretable model structures wherever possible
- Provide justification layers tied to clinically meaningful features
- Develop evaluation frameworks that measure trust and actionable insight
Reflection
High accuracy is gratifying, but for healthcare AI, it's only a starting point. Our models must communicate in ways clinicians understand and trust.