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Understanding data is the main purpose of data science and how to achieve it is one of the challenges of data science, especially when dealing with big data. Data visualization will facilitate LPPM in monitoring existing research and become a useful tool in research licensing decisions. Therefore, the role of Business Analyst in the LPPM division is required to visualize the data used, ie research, conduct various studies undertaken by the study program within a certain period, complete funds, and other details such as research title, research category, and name or conduct research others. To be able to issue licenses, especially those related to funds, LPPM should look at the existing track record so that the permits issued are not available funds and the funds to be budgeted will be sufficient for the needs of the research fund to be conducted next in the period. LPPM Division is in charge of arranging licensing conducted in the university environment, whether conducted by lecturers and students. Multimedia Nusantara University is one of the private universities that has a division of the Institute for Research and Community Service or called LPPM. Keywords: PACS, Computer Applications-General (Informatics), Diagnosis © RSNA, 2021. The implementation described could be helpful for assessing and monitoring AI performance in clinical practice. In all, a framework of processes and components for integrating AI algorithms into clinical workflows was developed.

All 14 survey respondents 'somewhat agreed' or 'strongly agreed' that the AI system was well integrated into the clinical workflow. A dashboard allowed monitoring of the AI system performance in real time. All AI results and corrections were successfully stored in a database for downstream use by the various integration components. The system enabled radiologists to correct 146 AI results, generating real-time corrections to the radiology report. The AI system processed 1748 lymphoscintigraphy examinations. Analyses included the assessment of the number of examinations processed, error rates, and corrections. A total of 14 users used the system (faculty radiologists and trainees) to assess the degree of satisfaction with the components and overall workflow.

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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. 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). Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms.