System and method for optimizing operations of a radiology service using ai powered gamification
AssigneeSiemens Healthineers AG
InventorsVivek Singh, Ankur Kapoor, Ingo Schmuecking, Scott Steingall, David Scholl, Dorin Comaniciu
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Abstract
Systems and methods for managing patient diagnostic and therapy workflows in a hospital and/or radiology centers. A radiology recommendation agent is trained using reinforcement learning and a simulation environment in which the agent takes actions and receives feedback from the simulation environment based on how its action affect the simulation environment over time.
Claims20
An AI powered gamification method for radiology center management, the method comprising:
- generating a simulation environment of a radiology center, the
- training an agent using the simulation environment in which the agent takes actions and receives feedback from the simulation environment based on how its action affect the simulation environment over time;
- and
- implementing the agent to provide real time recommendations that optimize the operation of the radiology system.
A system for radiology center management, the system comprising:
- an agent configured to provide recommendations for radiology scheduling and operations, the agent trained using reinforcement learning and a simulation environment in which the agent takes actions and receives feedback from the simulation environment based on how its action affect the simulation environment over time;
- and
- a graphical user interface configured to output the recommendations to a radiology operator.
A system for radiology center management, the system comprising:
- a medical imaging device configured to scan a patient;
- a scheduling system configured to schedule the scan and radiology personal for the scan;
- and
- a radiology recommendation agent configured to recommend one or more actions for the scheduling system and the medical imaging device, the radiology recommendation agent trained using reinforcement learning and a simulation environment in which the agent takes actions and receives feedback from the simulation environment based on how its action affect the simulation environment over time.
Dependencies are inferred from the claim text.