Designing sustainable medical devices requires balancing environmental, economic, and social demands, yet trade-offs across these pillars are difficult to identify using manual assessment alone. Current methods depend heavily on expert judgment, lack standardisation, and struggle to integrate diverse lifecycle data, which leads to overlooked conflicts and inconsistent evaluations. This paper introduces an AI-driven framework that automates conflict detection. Machine learning and natural language processing are used to extract trade-offs from design decisions, while Multi-Criteria Decision Analysis (MCDA) quantifies their magnitude through a composite sustainability score. The approach improves consistency, reduces subjective bias, and supports early design decisions. The results demonstrate how AI-assisted analysis provides scalable, data-driven support for sustainability evaluation in medical device development.