✦ Responsible AI Platform

SKCore AI Governance

AI that suggests, never decides — with a complete audit trail and human in the loop, always.

The responsible AI framework woven through every SKCore product — advisory-only architecture, PHI de-identification, per-hospital configuration, bias monitoring, and a full audit log of every AI suggestion made, accepted, or modified.

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Key Capabilities

What SKCore AI Governance Does

🎯

Advisory-Only Architecture

No AI output is ever written to the clinical record without explicit human approval. Every AI suggestion is presented as a draft for clinician review.

🔒

PHI De-Identification

All patient data is de-identified using NLP and rule-based stripping before any call to an external LLM. Re-identification risk assessment built in.

📋

Full AI Audit Trail

Every AI suggestion logged — prompt context, model used, output, clinician action (accept/modify/reject), and timestamp.

⚙️

Per-Hospital Configuration

Each hospital can independently enable or disable AI features per product, per role, and per workflow. API key managed securely in admin console.

⚖️

Bias Monitoring

Continuous demographic and clinical bias monitoring across all AI outputs — flagging if suggestions diverge by patient age, gender, or comorbidity.

🤖

LLM Routing Engine

Intelligent model routing — complex clinical reasoning uses high-capability models, documentation uses efficient models, reducing cost without sacrificing quality.

🌐

Enterprise AI Agent Workforce

Specialised AI agents organised across 15 departments. Department-level token budget controls. Full agent audit trail.

🔬

AI R&D Pipeline

Ongoing research in genomics, population health, federated learning, and ambient clinical intelligence — all governed by advisory-only principles.

AI Integration

AI in SKCore AI Governance

AI Governance itself uses AI to monitor AI outputs — detecting bias, drift, and quality degradation across all SKCore products.

  • ✓AI output quality monitoring — drift detection over time
  • ✓Demographic bias analysis across suggestion cohorts
  • ✓Model version tracking with performance comparison
  • ✓Token usage analytics per department and per feature
  • ✓Red-team adversarial testing of clinical AI prompts
AI Governance Principles
✓Advisory only — AI suggests, clinician decides
✓PHI de-identified before every model call
✓Every suggestion logged and auditable
✓Zero auto-saves without human approval
✓Bias monitoring across all AI outputs
Compliance & Standards

SKCore AI Governance — Standards & Frameworks We Design To

DPDP Act 2023ISO 42001-Aligned AI ManagementNITI Aayog AI PrinciplesCDSCO SaMD AI GuidanceWHO AI Ethics in HealthEU AI Act AlignedHIPAA Technical Safeguards
Get Started

See SKCore AI Governance in Action

Book a personalised demo tailored to your clinical workflow and hospital requirements.

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