The discussion on DICOM Standards: Helping or Hindering? focuses on the impact of these standards on obtaining relevant data for Artificial Intelligence (AI) applications in healthcare. Key points include the need to define DICOM standard tags for essential information like MRN, ACC#, and Body Part, as well as exploring free tags for institutions’ benefit.

Implementing more rigorous DICOM standards can enhance data cleanliness and improve data quality for AI systems. Revisiting the use of private tags and examining how DICOM standards influence various medical specialties like pathology and ophthalmology are essential considerations.

Reflecting on the positive role of DICOM standards in their current state and envisioning future advancements to better support enterprise imaging are crucial aspects of the discussion. Collaboration with Health Information Systems (HIS), Radiology Information Systems (RIS), Laboratory Information Systems (LIS), and Electronic Medical Records (EMR) is vital to standardize data synchronization across platforms. Encouraging collaboration among vendors to standardize data not only for storage but also for extracting clean data for AI and analytics is emphasized to advance healthcare technology integration.

Objectives

• Define the essential DICOM standard tags, including MRN, ACC#, and Body Part, and explain their role in enhancing data quality for AI applications in healthcare.
• Compare the impact of implementing more rigorous DICOM standards on data cleanliness and AI relevance versus using private tags and institutional customizations.
• Discuss how DICOM standards influence various medical specialties, such as pathology and ophthalmology, and identify future advancements needed to better support enterprise imaging and data synchronization across HIS/RIS/LIS/EMR platforms.