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[2602.22973] Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots
Summary
The paper presents a framework for improving AI diagnostic alignment in clinical settings by preserving AI-generated reports as immutable states for expert validation, enhancing safety and accuracy in medical decision-making.
Why It Matters
This research is significant as it addresses the critical need for reliable AI diagnostics in healthcare, particularly in safety-sensitive environments. By establishing a structured method for comparing AI outputs with expert evaluations, it enhances the transparency and trustworthiness of AI systems in clinical practice.
Key Takeaways
- Introduces a diagnostic alignment framework for AI in clinical settings.
- Demonstrates high concordance rates between AI-generated and expert-validated outcomes.
- Highlights the importance of structured evaluation in AI diagnostics.
- Suggests that traditional binary evaluations may underestimate alignment quality.
- Supports the need for traceable human-aligned evaluations in AI systems.
Computer Science > Artificial Intelligence arXiv:2602.22973 (cs) [Submitted on 26 Feb 2026] Title:Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots Authors:Dimitrios P. Panagoulias, Evangelia-Aikaterini Tsichrintzi, Georgios Savvidis, Evridiki Tsoureli-Nikita View a PDF of the paper titled Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots, by Dimitrios P. Panagoulias and Evangelia-Aikaterini Tsichrintzi and Georgios Savvidis and Evridiki Tsoureli-Nikita View PDF HTML (experimental) Abstract:Human-in-the-loop validation is essential in safety-critical clinical AI, yet the transition between initial model inference and expert correction is rarely analyzed as a structured signal. We introduce a diagnostic alignment framework in which the AI-generated image based report is preserved as an immutable inference state and systematically compared with the physician-validated outcome. The inference pipeline integrates a vision-enabled large language model, BERT- based medical entity extraction, and a Sequential Language Model Inference (SLMI) step to enforce domain-consistent refinement prior to expert review. Evaluation on 21 dermatological cases (21 complete AI physician pairs) em- ployed a four-level concordance framework comprising exact primary match rate (PMR), semantic similarity-adjusted rate (AMR), cross-category alignment, and Comprehensive Concordance Rate (CCR). Exact agreement reached 71.4% and remained unchanged under sem...
