An AI-Based Framework for Assessing Sustainability Conflicts in Medical Device Development

TL;DR

This paper presents an AI-based framework that automates the detection and quantification of sustainability conflicts in medical device development, enhancing consistency and reducing bias in sustainability assessments.

Contribution

It introduces a novel AI-driven approach combining machine learning, NLP, and MCDA to automate conflict detection and scoring in sustainability evaluations.

Findings

Automates conflict detection in medical device sustainability.

Improves consistency and reduces subjective bias.

Provides scalable, data-driven support for early design decisions.

Abstract

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.