• Commercial AI Models in Radiology Create New Growth Opportunities
    The intensely competitive landscape necessitates the repositioning of value through the acceptance of new business models

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    As artificial intelligence (AI) solutions gain acceptance in the imaging fraternity and among senior leaders in the health systems, competition has intensified and necessitated the creation of differentiation strategies for companies that want to improve their revenue and sustain growth. The rapid proliferation of medical imaging AI companies has led to the availability of a plethora of solutions in the market; however, hospitals are not able to access these solutions (and vice versa). Owing to the shift from fee-for-service models to value-based reimbursement, intrinsic factors that drive adoption (improved sensitivity and specificity, reduced reporting and interpretation time etc.) will become less important to end users. AI vendors should design their solutions and their pricing strategies to align with the value delivered in the overall care pathway.

    Declining reimbursements and operating margins for providers and the commoditization of AI solutions creates pricing pressure on imaging AI vendors necessitating the creation of new revenue streams. Customers are demanding a wide range of profound solutions, which necessitates that solution providers come up with new partnership models. Frost & Sullivan observes that none of the stakeholders in the imaging AI value chain can survive in isolation due to the short product cycles and the rapid technological obsolescence. The launch of platforms and marketplaces by imaging original equipment manufacturers (OEMs) and others has paved the way for new distribution channels and commercial models. The software-as-a-service (SaaS) marketplace (mainly cloud-based) is meant to simplify medical imaging providers' access to various independent software vendors' applications without having to contract and engage individually with each vendor. AI in imaging will see increasing adoption in the medium term (2-4 years) as these platforms help end users improve integration and orchestration capabilities for workflows, thereby benefiting third-party AI vendors as well. As pressure on radiologists increases due to high scan volumes and complex cases, vendors should focus on condition-based packages that can be integrated with operational processes. AI Vendors can offer comprehensive condition-specific packages by partnering with OEM platforms or marketplace to decrease the cost of sale, improve market access, and benefit from new pricing models and integration facilities.


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