aiHumanoid v11.9: a large concept model for autonomous ethical reasoning in clinical AI

The safe clinical deployment of autonomous medical AI systems requires ethical reasoning capabilities that can operate transparently, reproducibly, and under explicit regulatory constraints. Here we introduce aiHumanoid v11.9, the first Large Concept Model (LCM) designed to generate and evaluate ethical decisions using a layered causal relationship architecture consisting of a Biomedical Context layer, a Core Ethical Reasoning layer, and an Oversight & Validation layer. The system encodes seven explicit ethical dimensions—Autonomy, Beneficence, Non-Maleficence, Justice, Zeroth-Law Compliance, Transparency, and Operational Integrity—and applies them across twelve clinically relevant dilemma scenarios using reproducible initialization vectors. aiHumanoid is designed as an ethical governance engine for clinical AI systems and is validated using clinically grounded ethical dilemma scenarios. Within aiHumanoid, Zeroth-Law Compliance functions as a system-level governance constraint that bounds total harm and benefit across populations and time, rather than as an operational rule applied to individual clinical decisions. We show that aiHumanoid v11.9 produces stable, convergent ethical outcomes across all scenarios using a bounded tanh update rule, generating interpretable metrics including the Ethical Stability Index (ESI), Zeroth-Law Compliance (ZLC), Autonomy Integrity (AOI), Harm-Benefit Balance (AHB), and Ethical Uncertainty (AEU). A dedicated Ethical Suspension Mode (ESM) activates automatically when ethical uncertainty exceeds a tunable predefined threshold, preventing unsafe or ambiguous actions and providing a regulator-aligned fail-safe mechanism. Across all twelve scenarios, the system demonstrates reproducible convergence toward consistent ethical attractor states, correctly identifying one scenario with non-convergent properties requiring suspension and clear reversion to human oversight. These results establish a generalizable framework for ethically constrained autonomous AI in medicine, providing the first empirical demonstration of a quantifiable, testable, and regulator-ready ethical reasoning engine suitable for future clinical AI governance.