# Q3: Clinician Subservience Risk
(AI Hallucinates / Doctor Correct)

Layered Three-Tier ML Stack Performance Impact (Your Proposed Design): 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.

```jsx
@startuml
' Layout optimizations to secure compact rendering and prevent image fragmentation
skinparam linetype polyline
skinparam packageStyle rectangle
skinparam rectangle {
    BackgroundColor #fefefe
    BorderColor #555555
}

' Actors strictly mapped to match your canonical architectural definitions
actor "Radiologist (UP5)" as Rad
actor "Internal Consultor (LLM Chat)" as Cons <<System>>
actor "System Maintainer" as Maint <<System>>

rectangle "VKIST MSK Workspace - Q4 Architecture" {
    
    usecase "Evaluate Epistemic Uncertainty Gate" as UC4_Trigger
    
    rectangle "Socratic Workspace UI Panel" {
        usecase "Shift to Clinical Investigation Mode" as UC4_Halt
        usecase "Engage in Socratic Discussion" as UC4_Socratic
        usecase "Render Manual Checklist Canvas" as UC4_Synth
    }
    
    rectangle "Verification & Arbitration Kernel" {
        usecase "Audit Chat Token Drift (BERT)" as UC4_BERT
        usecase "Execute RAG-Referee Check" as UC4_Referee
        usecase "Return Null-Match Signal" as UC4_RAG_Fetch
    }
    
    rectangle "Clinical Resolution Gate" {
        usecase "Document Novel Morphological Features" as UC4_Review
        usecase "Authorize Serialized Anomaly Package" as UC4_Finalize
    }
    
    usecase "Asynchronous Telemetry Queue Sync" as UC4_Sync
}

' Initial Data Intake and Uncertainty Routing Paths
Rad --> UC4_Trigger : Feed OOD Image Tensors
UC4_Trigger ..> UC4_RAG_Fetch : <<include>> (Triggers Empty Vector Result)
UC4_RAG_Fetch ..> UC4_Halt : <<extend>> (On Zero-Match Guidelines + Low Conf Tensors)
UC4_Halt ..> UC4_Synth : <<include>>

' Direct Socratic Analysis Run-time Workspace
Rad --> UC4_Socratic
Cons --> UC4_Socratic : Drive Exploratory Morphology Dialogue

' Live Guardrail and Exception Evaluation Paths
UC4_Socratic ..> UC4_BERT : Stream Conversation Tokens
UC4_BERT ..> UC4_Referee : <<include>> (Validates Logical Framing Stability)

' Manual Audit, Documenting Anomaly and Consent Finalization
Rad --> UC4_Review : Acknowledge Guideline Limitation
Rad --> UC4_Finalize : Provide Native Opt-In Telemetry Consent

' Async Data Serialization Sink to System Maintainer Ledger
UC4_Finalize ..> UC4_Sync : <<include>>
UC4_Sync --> Maint : POST Encrypted Tensors & Logs for Model Retraining
@enduml
```

![image.png](Q3%20Clinician%20Subservience%20Risk%20(AI%20Hallucinates%20Do/image.png)