Hybrid Neutrosophic-AI Framework for Biomedical and Financial Decision-Making under Uncertainty

Uncertainty remains a key challenge to decision making from data driven decisions, especially in biomedical and financial systems where data is often incomplete, inconsistent, or inadequate. Many of the traditional Artificial Intelligence and Probabilistic models have had particularly successful work in prediction and classification, but they are often underperforming when confronted with indeterminacy conditions in which truth values cannot be accurately defined. To overcome this limitation, this paper describes a Hybrid Neutrosophic-AI Framework that combines the interpretive power of Neutrosophic Set Theory with the adaptive capabilities of modern AI. The proposed framework encapsulates uncertainty by using three dimensions: truth (T), indeterminacy (I), and falsity (F) to give better quantification of the conflicted and contradictory data. It is composed of neutrosophic logic, machine learning architectures such as deep neural networks, Bayesian classifiers and reinforcement learning models, and features that are integrated into a composite system which learns and adapts even when difficult or contradictory conditions. It is used for disease diagnosis and prediction in the biomedical arena, since incomplete patient records and noisy sensors often hamper accuracy. Within the financial world, the framework primarily focuses on portfolio optimization, credit risk assessment and fraud investigation in highly volatile markets. Comparison of conventional fuzzy-AI and probabilistic systems suggests that a hybrid model significantly improves uncertainty tolerance, interpretability and decision accuracy in both domains. The results also support the notion that neutrosophic reasoning contributes to a plausible and robust computational structure that bridges the gap between symbolic and statistical intelligence, and the incorporation of neutrosophic reasoning in AI decision engines.