Pricing Simulator for AI-Based Diagnostic Decision Support

Artificial intelligence is increasingly embedded in diagnostic decision support systems (DDSS), yet pricing and reimbursement remain heterogeneous and are often only weakly linked to diagnostic complexity, implementation burden, and recurring vendor costs. We present the methodological basis for PricingApp, a transparent pricing simulator for vendor-provider procurement decisions. The application combines a six-phase workflow with an AI-Score. The AI-Score is an unweighted sum of 20 equally weighted items grouped into four dimensions: data complexity, disease complexity, clinical question complexity, and degree of AI involvement. The total score (20-100) is mapped to low, medium, high, and very high complexity bands that parameterize complexity-sensitive pricing. Beyond complexity, the simulator captures market context, implementation effort, benefit measurability, AI inference-cost intensity, and cost structure. By making pricing assumptions explicit, testable, and auditable, the approach supports structured comparison of license/subscription, usage-based, hybrid, and pricing models based on the Diagnostic AI Contribution Score (DACS).