Background: Multidisciplinary team (MDT) meetings are central to breast cancer management but operate under increasing time pressure and resource constraints. Artificial intelligence (AI)–based clinical decision support tools may offer a useful adjunct to MDT decision-making. Aim: To compare breast cancer management recommendations generated by a human MDT with those produced by an AI-based clinical decision support system (OpenEvidence), and to explore potential added value. Methods: A retrospective analysis was conducted on 100 de-identified breast cancer cases discussed at a Western Australian tertiary hospital breast MDT. The same clinical, pathological, and staging information presented to the MDT was entered into OpenEvidence to generate AI-based recommendations. AI outputs were compared with contemporaneous MDT decisions across surgical management, systemic therapy, radiotherapy, and overall treatment intent, with qualitative assessment of agreement and differences. Results: AI-generated recommendations were frequently aligned with human MDT decisions, particularly for guideline-driven management pathways. Differences most commonly reflected contextual factors incorporated by the MDT, including patient comorbidities, surgical feasibility, patient preferences, and local resource considerations. A key advantage of AI was its ability to analyse complete clinical datasets rather than condensed MDT summaries. AI outputs also provided explicit guideline-referenced reasoning, offering greater educational value for non-specialists than brief outcome-focused MDT documentation. Conclusion: OpenEvidence shows promise as a decision-support adjunct to breast cancer MDTs. While AI cannot replace clinician-led multidisciplinary judgement, its capacity for comprehensive analysis and transparent reasoning may enhance consistency, education, and decision support within MDT workflows.