This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.

Background/Objectives: Clinical decision support systems (CDSS) consisting of computerized physician order entry (CPOE) and oncology pathways serve as the foundation of high-quality cancer care. However, the resources needed to develop and maintain these systems have not been characterized for oncology enterprises. Methods: Missing CPOEs for oncology treatments were identified by comparing current CPOEs to those in the Elsevier ClinicalPath treatment pathways. New FDA oncology drug approvals were used to estimate ongoing CPOE build needs. The impacts of missing orders on clinicians and pharmacists were surveyed. Benchmarking from two other large oncology enterprises supported new resource needs to build and maintain CPOE in our EPIC Beacon system. A program to manage oncology pathways and CPOE for an enterprise using EPIC was developed and implemented. Results: Resources and impacts of CDSS for oncology enterprises are described. Program management is described. Conclusions: Updating and maintaining CDSS for an oncology enterprise requires program management and resources for which a framework has not been previously reported. We present this work as a framework for others to understand the components and resources for budgeting and management of CDSS. We report on the work of the City of Hope enterprise using the EPIC electronic health record.