De Fauw et al. (2018)
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A landmark Nature Medicine paper illustrating how interpretability and human-AI collaboration concerns manifest in high-stakes medical AI deployment; often cited in discussions of safe AI integration into clinical workflows.
Metadata
Summary
De Fauw et al. present a deep learning system that diagnoses over 50 retinal diseases from OCT scans with expert-level accuracy by separating segmentation and classification into two sequential neural networks. The system achieves performance matching or exceeding world-leading retinal specialists and provides interpretable, clinically actionable referral recommendations. This work demonstrates both the promise and the interpretability challenges of deploying AI in high-stakes medical decision-making.
Key Points
- •Two-stage architecture: first network segments retinal anatomy into 3D maps, second network classifies disease from those maps, improving interpretability.
- •Achieved expert-level diagnostic performance across 50+ retinal conditions, matching or surpassing world-leading ophthalmologists.
- •The segmentation-first approach allows clinicians to verify intermediate representations, partially addressing the 'black box' concern in medical AI.
- •Demonstrates generalization across different OCT scanner types, a key practical challenge for clinical deployment.
- •Raises important questions about human-AI collaboration, appropriate trust calibration, and responsibility in automated medical referrals.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Human Hybrid Systems | Approach | 91.0 |
Cached Content Preview
Clinically applicable deep learning for diagnosis and referral in retinal disease | Nature Medicine
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Subjects
Diagnosis
Eye manifestations
Machine learning
Three-dimensional imaging
Abstract
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.
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