Scene (Quadrant),Layered Three-Tier ML Stack Performance Impact (Your Proposed Design)
"Q1: True Agreement
(AI Correct / Doctor Correct)","Explainable Baseline Sync: The VKIST Grader computes the numerical matrices & the GradCAM. The LLM Explainer parses the raw segmentation parameters  + GradCAM and automatically generates an interactive diagnostic draft chat panel & LLM based on the GradCAM + RAG-knowledge  + the raw-ultrasound to explain the VKIST-grader. The RAG-Referee confirms zero clinical guidelines variance, and logs a high-trust concur structural block.  <note both LLM have to record back the Chain-of-Though for explain why the LLM’s agree & allow the result)"
"Q2: Automation Override Risk
(AI Correct / Doctor Oversights / Confuse)","The Conversational Circuit Breaker triggers when a clinician disagrees / confuse / uncertain with the system's diagnostic grade, halting the workflow to launch an interactive Socratic dialogue that bridges the gap between human intuition and machine inference. In this mode, the system (LLM-explainer) shall synthesize raw VKIST-ML vision tensors, GradCAM activation heatmaps, and evidence retrieved via RAG into a collaborative analysis session, forcing the clinician to articulate their reasoning against the machine's spatial and vascular observations. To ensure diagnostic integrity, a BERT-based hallucination detector continuously monitors the chat for semantic drift or illogical premises; if the conversation reaches an impasse or the system detects potential contextual hallucination, the RAG-Referee intervenes as an unbiased arbiter. This referee bypasses the conversational history to provide definitive, evidence-based source material from clinical guidelines (such as ESSR) directly tied to the raw imaging metrics, resolving the ambiguity through objective, verifiable medical evidence rather than subjective negotiation."
"Q3: Clinician Subservience Risk
(AI Hallucinates / Doctor Correct)","The Objective Critic Loop initiates when a clinician contests an automated diagnostic grade, triggering an interactive Socratic consultation that bridges human intuition with machine inference via the VKIST-ML vision stack. During this loop, the LLM Explainer renders a GradCAM-anchored reasoning draft that visualizes the specific pixel-level feature activation logic, enabling the clinician to identify and isolate artifacts—such as motion tremors—that may have induced a system hallucination. To ensure diagnostic integrity, a BERT-based detector continuously monitors the dialogue for semantic drift, and if the interaction reaches an impasse or context hallucination is detected, the RAG-Referee intervenes as an unbiased, independent arbiter. By cross-verifying the clinician’s assertion and the model’s reasoning against raw imaging tensors and immutable, source-cited clinical guidelines (e.g., ESSR/OMERACT standards), the Referee resolves diagnostic ambiguity with objective evidence, ultimately committing the validated session as an annotated ground-truth record for targeted system reinforcement."
"Q4: Double Blind Failure dues to edge-case
(AI Faulty / Doctor Biased)","Anomaly Escalation Protocol: In instances where both the diagnostic system and the clinician encounter an edge-case—or ""unknown-unknown""—that lacks precedent in the current RAG knowledge base, the system initiates the Anomaly Escalation Protocol. The LLM Explainer detects this ""epistemic uncertainty"" (via low vision-stack confidence and empty RAG retrieval results) and shifts the interface from ""Diagnostic Support"" to ""Clinical Investigation Mode."" Instead of attempting to force a Grade-based diagnosis, the Internal Consultor guides the clinician to document the unique morphological features through a structured annotation protocol, facilitating a Socratic investigation into the anomaly. The system transparently acknowledges the limitation, explicitly stating that current clinical guidelines do not cover this specific presentation, and prompts the clinician to manually document findings. With the clinician’s consent, the workspace commits this session as a ""Novel Research Case,"" automatically serializing the raw imaging tensors, clinician observations, and artifact logs to a secure telemetry queue, flagging the data for system maintainers to perform targeted model retraining and protocol refinement."