Diagnostic Decision Support Systems (DDSS) represent a critical technology for providing high-quality, evidence-based, and efficient care in modern healthcare systems. Integrating artificial intelligence (AI) into such systems offers promising ideas, but also introduces complex challenges, particularly in pricing. This study examines how pricing models for AI-enabled DDSS in Germany should be designed to meet economic, regulatory, technical, and ethical requirements. A structured evaluation framework is developed based on a systematic analysis of existing pricing models, a market overview of manufacturers, and consideration of technological and regulatory frameworks (e.g., EU-AI-Act). The findings indicate that hybrid and usage-based leasing models are especially promising, as they offer flexibility, scalability, and transparency. However, the growing complexity of AI inference processes requires the development of new metrics to evaluate the added value of such systems. The study emphasizes the need for sustainable pricing strategies that incorporate data protection, certification processes, technical infrastructure, and user acceptance. The results offer practical implications for providers, service providers, and policy makers.