Author: Brad Genereaux

See https://link.springer.com/article/10.1007/s10278-018-0073-z. Published 2018-05-10.

This paper describes why and how DICOM, the standard that has been the basis for medical imaging interoperability around the world for several decades, has been extended into a full web technology-based standard, DICOMweb. At the turn of the century, healthcare embraced information technology, which created new problems and new opportunities for the medical imaging industry; at the same time, web technologies matured and began serving other domains well. This paper describes DICOMweb, how it extended the DICOM standard, and how DICOMweb can be applied to problems facing healthcare applications to address workflow and the changing healthcare climate.

See https://pubs.rsna.org/doi/10.1148/ryai.2021210013. Published 2021-08-04.

Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms. In this report, generalizable components for deploying AI systems into clinical practice are described that were implemented in a clinical pilot study using lymphoscintigraphy examinations as a prospective use case (July 1, 2019-October 31, 2020). Deployment of the AI algorithm consisted of seven software components, as follows: (a) image delivery, (b) quality control, (c) a results database, (d) results processing, (e) results presentation and delivery, (f) error correction, and (g) a dashboard for performance monitoring. A total of 14 users used the system (faculty radiologists and trainees) to assess the degree of satisfaction with the components and overall workflow. Analyses included the assessment of the number of examinations processed, error rates, and corrections. The AI system processed 1748 lymphoscintigraphy examinations. The system enabled radiologists to correct 146 AI results, generating real-time corrections to the radiology report. All AI results and corrections were successfully stored in a database for downstream use by the various integration components. A dashboard allowed monitoring of the AI system performance in real time. All 14 survey respondents “somewhat agreed” or “strongly agreed” that the AI system was well integrated into the clinical workflow. In all, a framework of processes and components for integrating AI algorithms into clinical workflows was developed. The implementation described could be helpful for assessing and monitoring AI performance in clinical practice.