Enterprise AI Analysis
The explainable Al dilemma under knowledge imbalance in specialist Al for glaucoma referrals in primary care
Specialized AI in healthcare offers immense potential, yet its integration faces unique challenges when supporting generalist providers. This analysis explores how explainable AI impacts human-AI team performance and trust in glaucoma referrals, revealing critical insights for future enterprise AI deployments.
Executive Impact Snapshot
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Human-AI Team Accuracy
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Human-Alone Baseline Accuracy
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AI-Alone Potential Accuracy
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Over-Claims Reduction with Scoring-based XAI
Deep Analysis & Enterprise Applications
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60%
Human-AI Team Accuracy Achieved
Context: Human-AI teams achieved higher accuracy (60%) than humans alone (51%), but explanations did not significantly enhance performance over AI without explanations. This highlights the potential of AI to augment human decisions, yet also points to the challenges in designing effective explanations.
80%
AI-Alone Accuracy
Context: Despite the improvement with AI assistance, human-AI teams still underperformed the AI alone (80% accuracy) across the study vignettes. This performance gap suggests further research is needed to achieve synergistic performance where combined human-AI exceeds AI alone.
Comparative Impact of AI Explanation Types on Performance
AI support enhanced referral accuracy, but explanations (Feature Importance and Scoring-based) did not significantly outperform AI without explanations, and did not close the gap with AI-alone performance.
| Explanation Type | Key Characteristics | Performance Impact (Accuracy vs. Human Alone) |
|---|---|---|
| No Explanation (Black Box) | AI prediction only, no additional rationale provided. | Improved human accuracy by 7% (58% vs 51%) |
| Feature Importance (Post-hoc) | Top 3 contributing factors shown (e.g., SHAP values). | Improved human accuracy by 9% (60% vs 51%) |
| Scoring-based (Intrinsic) | Visual risk scorecard, explicit formula, feature intervals, coefficients. | Improved human accuracy by 11% (62% vs 51%) |
Increased
Over-reliance with Post-hoc XAI
Context: Post-hoc explanations (Feature Importance) significantly increased participants' agreement with incorrect AI predictions compared to no explanations (M=0.92 vs M=0.77). This indicates a potential for misplaced trust and over-reliance with certain XAI types.
Anchoring Bias and Trust Calibration Challenges
Participants made fewer adjustments to AI-suggested referral categories when presented with either post-hoc or intrinsic explanations. Smaller AI deviation scores suggest anchoring to AI predictions, potentially not considering alternative referral options. This highlights the challenge of calibrating trust; while AI-enabled decision support improves overall human performance, explanations alone may not prevent over-reliance or under-reliance without further interaction design. Users struggled to determine when to trust the algorithm or rely on their judgment.
Enterprise Impact: Designing interactions that encourage an initial human assessment before AI review could mitigate these biases and improve overall team performance, fostering a more effective complementary distribution of knowledge. Without proper trust calibration, AI could introduce new risks.
Significantly Higher
Perceived Helpfulness of Scoring-based XAI
Context: Scoring-based Explanations were rated significantly higher for helpfulness and willingness to use in clinical practice compared to AI without explanations. Their transparent rules, comprehensive information, and graphical representations likely contributed to this positive perception.
Enterprise Process Flow: Glaucoma Referral AI
Our AI system was designed to predict the risk of glaucoma surgery (H40 diagnosis code) over various time horizons, mapping this to corresponding referral timeframes. The workflow integrates diverse data types and model outputs to assist primary eye care providers.
Patient Data Collection (Clinical, VF, OCT)
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Vision Transformer for Feature Extraction
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Classifier for Glaucoma Surgery Probability
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Mapping to Referral Timeframes (0-3, 3-12 months, No Referral)
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AI Recommendation & Explanation Generation
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Optometrist Review & Final Decision
AI Explanation Mechanisms Utilized
Two primary types of explanations were developed and evaluated: post-hoc for a deep learning model, and intrinsic for a risk scorecard model, each offering different insights into AI reasoning.
| Mechanism | Model Type | Key Features | Explanation Type |
|---|---|---|---|
| SHAP (Feature Importance) | Vision Transformer (DLM) | Top 3 risk factors (e.g., High PSD, Low RNFL Thickness, Low MD) | Post-hoc (model-agnostic) |
| Risk Scorecard | Logistic Regression (Simplified) | Visual score, explicit formula, feature intervals, coefficients | Intrinsic (inherently interpretable) |
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