As artificial intelligence (AI) solutions gain acceptance in the imaging fraternity and among senior leaders in the health systems space, 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). 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. Owing to the shift from fee-for-service models to value-based reimbursement, intrinsic factors that drive adoption (improved sensitivity and specificity and reduced reporting and interpretation time) 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. Predictive solutions and the identification of new protocol or indications for imaging will decrease readmissions and enable optimum resource utilization (equipment, manpower, and financial) and align with the goals of value-based care. 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. 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.--BEGIN PROMO--

Research Highlights

This Frost & Sullivan study examines the following:

• Strategic imperatives for vendors
• Key market trends and impact of AI on radiology
• Scenarios driving the adoption of AI among providers
• Types of value creation of AI in radiology
• Adoption of AI and functional prioritization by radiologists
• Evolution/progression of pricing models for AI tools
• Demand-side factors that influence the shift in pricing models
• Value positioning of various pricing models
• Top growth opportunities

Author: Srinath Venkatasubramanian