Self-Healing Artificial Intelligence: Autonomous Detection and Correction of Machine Learning Model Failures

Artificial intelligence (AI) systems are increasingly deployed in environments whose data distributions, operating conditions, user behaviour, sensors, and decision requirements change after deployment. A model that is accurate at release can therefore become unreliable without an obvious software fault. This paper develops a research-based conceptual framework for self-healing artificial intelligence: an operational AI capability that continuously detects model degradation, diagnoses its likely cause, selects a targeted corrective action, tests that action under explicit safety constraints, and either deploys or rejects the repair. The study synthesises literature on concept drift, dataset shift, uncertainty-aware monitoring, continual and online learning, MLOps, and recent self-healing machine learning. The analysis distinguishes data drift, concept drift, label shift, data-quality failures, calibration failure, model staleness, and pipeline faults because different failure mechanisms require different responses. A four-stage healing cycle-monitoring, diagnosis, adaptation, and testing-is integrated with production safeguards such as shadow evaluation, rollback, confidence thresholds, audit trails, and human escalation. The paper further proposes an evaluation framework that measures not only post-repair predictive performance but also detection delay, false-alarm rate, diagnosis quality, recovery time, intervention cost, and safety regressions. The synthesis shows that autonomous retraining alone is not equivalent to self-healing AI: robust self-healing requires reason-aware diagnosis and evidence that a proposed intervention improves the system under the changed data-generating process. The paper concludes with a research agenda for reliable, explainable, resource-aware, and governance-compatible self-healing AI.