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# I Built a Medical AI Model That Knows When to Say “I’m Not Sure”

**[Medium](https://daily.dev/sources/medium_js)** · 9 min read · 0 upvotes · 0 comments

## Summary

An MSc student built a diabetic retinopathy detection system using EfficientNetB0 that incorporates uncertainty estimation via Monte Carlo Dropout and Deep Ensembles, plus a rejection mechanism that flags uncertain predictions for human review instead of forcing automated decisions. MC Dropout halved the Expected Calibration Error (from 0.0457 to 0.0223) compared to the baseline, and at a 23% rejection rate, accepted-set accuracy improved by 6.3 percentage points. Key lessons include: preprocessing quality outweighs architecture choices, calibration matters more than raw accuracy in medical AI, class imbalance silently degrades models, and a system that can say 'I don't know' is more clinically trustworthy than one that always outputs a confident answer.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@pmpdilshan/i-built-a-medical-ai-model-that-knows-when-to-say-im-not-sure-7462a336b8ce>

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---

Tags: [#python](https://daily.dev/tags/python), [#deep-learning](https://daily.dev/tags/deep-learning), [#tensorflow](https://daily.dev/tags/tensorflow)

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