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Related Experiment Video

Updated: Jul 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Pricing Simulator for AI-Based Diagnostic Decision Support.

Jan Kirchhoff1,2, Fabian Berns2, Christian Schieder3

  • 1DigiHealth Institute, Neu-Ulm University of Applied Sciences, Wileystraße 1, 89231 Neu-Ulm, Germany.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

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Pricing artificial intelligence in diagnostic decision support systems (DDSS) is complex. PricingApp offers a transparent simulator using an AI-Score to standardize pricing based on complexity, aiding vendor-provider procurement.

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Medical Decision Support

Background:

  • Diagnostic decision support systems (DDSS) increasingly utilize artificial intelligence (AI).
  • Current pricing and reimbursement models for AI-DDSS are inconsistent and poorly correlated with factors like complexity and cost.
  • Lack of standardized pricing hinders effective procurement and vendor negotiations.

Purpose of the Study:

  • To introduce PricingApp, a transparent pricing simulator for AI-DDSS procurement.
  • To provide a methodological basis for evaluating and comparing AI-DDSS pricing models.
  • To support informed vendor-provider procurement decisions by making pricing assumptions explicit and auditable.

Main Methods:

  • Development of PricingApp, a six-phase workflow simulator.
Keywords:
Artificial IntelligenceDiagnostic Decision Support SystemsHealth Technology AssessmentPricing ModelsReimbursement

Related Experiment Videos

Last Updated: Jul 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

  • Introduction of an AI-Score, an unweighted sum of 20 items across four complexity dimensions: data, disease, clinical question, and AI involvement.
  • Integration of market context, implementation effort, benefit measurability, AI inference costs, and cost structure into the pricing model.

    • Main Results:

    • The AI-Score ranges from 20-100, mapped to low, medium, high, and very high complexity bands.
    • PricingApp parameterizes complexity-sensitive pricing structures.
    • The simulator facilitates structured comparison of various pricing models, including license/subscription, usage-based, and hybrid, using the Diagnostic AI Contribution Score (DACS).

    Conclusions:

    • PricingApp offers a transparent and auditable framework for pricing AI-DDSS.
    • Standardizing pricing evaluation through complexity assessment and cost factors can improve procurement efficiency.
    • The tool supports informed decision-making for both vendors and providers in the AI-DDSS market.