Across every organization, there are varying structures, services, priorities, IT infrastructures, PACS vendors, and even politics. Consequently, the priorities, potential benefits, and challenges that are associated with designing a medical imaging AI adoption strategy vary significantly among the departments within an organization.

For this reason, it is essential to consider how each department and its associated priorities will be impacted by AI. It is also vital to identify role-specific challenges faced by administrators, IT teams, and radiologists at the project outset to ensure they are thoroughly considered and methodically mitigated throughout each phase of an AI strategy rollout.



While administrators are primarily focused on budgets and ROI, they are also responsible for ensuring that AI is efficiently and rapidly implemented and operationalized within their organization. Given that budgeting and IT cycles are often long, it can take anywhere from 6 months to 2 years to identify, evaluate, select, contract, fund, deploy, and integrate an individual AI solution.

Prioritizing and creating a roadmap for algorithm deployment requires thorough and careful consideration. Therefore, administrators must be meticulous in their approach when comparing the procurement and support costs of an AI strategy against its potential benefits for the entire organization. It can be tempting to focus on top-line revenue and evaluate algorithms based solely on their direct impact on reimbursement. However, greater value often lies in the system-wide impact of AI and its value potential for quality improvements and cost savings such as:

  • Increasing radiologist productivity and confidence,
  • Improving modality utilization and Service Level Agreement (SLA) adherence,
  • Reducing readmissions and length of stay.



From an IT perspective, departments are responsible for setting up and maintaining AI infrastructure, as well as providing relevant modelling data to AI algorithms. All of this must be managed and prioritized alongside their existing organizational initiatives and substantial workload.

When assessing AI deployment models, it is critical to consider the solution’s potential impact on IT resources across all phases of evaluation, implementation, and support.

In a platform approach, AI models come pre-validated and integrated into a consolidated architecture. This can greatly simplify AI infrastructure, data access, systems integration, interoperability, and deployment. It can also greatly simplify the addition of new algorithms into the imaging ecosystem and streamline support processes by centralizing updates and maintenance, consolidating monitoring and oversight, and providing a singular interface for support engineers.


Clinicians and Radiologists

First and foremost, clinicians and radiologists must be able to focus on clinical quality and accuracy, timeliness of results delivery, and productivity. Study backlogs and growing workloads mean radiologists are increasingly concerned that the quality and efficiency of their interpretations will be negatively impacted by fragmented AI algorithms that have differentiated tools, user interfaces, and workflows.

AI models should complement and augment (not fragment or disrupt) departmental processes and radiologist reading patterns. It is therefore vital to ensure that AI algorithms fit seamlessly into radiologist workflows to reduce variability and improve imaging workflow quality and consistency – ultimately resulting in faster, more accurate diagnostic decision making.

Are you interested in learning more about mitigating the challenges faced by clinical, administrative, and IT stakeholders when designing an AI strategy? Click here to download our AI Buyer’s Guide, which highlights the key roles involved in AI decision making and discusses how to successfully evaluate, select, and implement a future-proof platform.


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