Many healthcare organizations report that traditional worklist systems rely on rigid rules that ignore critical context, radiologist specialization, current workload, fatigue levels, and case complexity. This creates a persistent challenge: radiologists cherry-pick easier, higher-value cases while avoiding complex studies, leading to diagnostic delays and increased costs. Research across 62 hospitals analyzing 2.2 million studies found that inefficient case assignment causes 17.7-minute delays for expedited cases and costs of $2.1M–$4.2M across hospital networks. The root cause is straightforward: traditional radiology worklist systems rely on rigid, rule-based engines that ignore the context that matters most — radiologist specialization, current workload, fatigue levels, and case complexity. In this post, we’ll show how to build an radiology workflow optimization with AI agents on Amazon Bedrock AgentCore and Strands Agents SDK .

Radiologist worklist systems rely on deterministic, rule-based engines that route studies according to predefined logic. Static specialty matching ignores context, such as whether the available radiologist has been interpreting complex cases for several consecutive hours or whether a straightforward follow-up scan truly warrants subspecialist attention. Workload balancing responds to current queue depth rather than anticipating demands based on case complexity, estimated interpretation time, or physician fatigue patterns. Most critically, no learning occurs when deterministic rules produce suboptimal assignments, the same inefficient patterns repeat until someone manually updates the underlying logic. In this post, you can learn how to:

By moving beyond rigid, deterministic rules toward Agentic AI that truly understands our subspecialties, we are witnessing a paradigm shift that elevates radiology workflow from simple task management to truly autonomous orchestration. The right subspecialist is seamlessly matched with the right case at the right time, freeing radiologists to focus entirely on diagnostic excellence rather than navigating the queue. Radiology Partners recognizes this as a mission-critical workflow capability and is partnering with AWS to adopt Agentic AI for intelligent workflow optimization.

An AI agent is an autonomous software component that can perceive its environment, reason about goals, and take actions to achieve them. In your radiology workflow optimization, a network of specialized AI agents collaborates to orchestrate complex clinical workflows from start to finish. Each agent handles specific tasks within the workflow. Agents coordinate across specialties and adapt to deliver optimal outcomes for patients and team. AI agents on Bedrock AgentCore evaluate multiple factors simultaneously such as radiologist specialization, current workload, fatigue patterns, case complexity, clinical urgency, and availability to make optimal case assignments. The AI models powering the agents are foundation models (FMs) available through Amazon Bedrock. The system continuously learns from historical patterns and adapts to changing conditions, minimizing the incentive structures that drive cherry-picking behavior.

This section walks you through the solution architecture and implementation of accelerating radiology imaging workflows by intelligently optimizing exam prioritization and radiologist assignment. A sample exam assignment output from the intelligent worklist orchestrator is shown in the following figure. A knee MRI study arrives in picture archiving and communication system (PACS) and needs to be assigned. The agentic worklist optimization system suggests the primary assignment along with rationale as below.

Tool requests are routed to the MCP Server within the AgentCore Runtime, which exposes multiple backend tools essential to the workflow. These integrated tools include access to Clinical data API for accessing patient records and medical histories from electronic health record (EHR) systems and the Rad calendar for retrieving radiologist scheduling information through MCP server. The tools will use existing enterprise Imaging APIs for direct imaging study access from PACS via OpenAPI specifications.