ArticleJournal of imaging informatics in medicine2025
OsiriXGPT: An Innovative AI Co-pilot Plug-In for Seamless Deployment of Generative AI Models in Scan-to-Scan Reporting Workflows.
Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Generative Artificial Intelligence (GenAI) has the potential to transform radiology by reducing reporting burdens, enhancing diagnostic workflows and facilitating communication of complex radiological information. However, research and adoption remain limited due to the lack of seamless integration with medical imaging viewers. This study introduces OsiriXgrpc, an open-source API plug-in that bridges this gap, enabling real-time communication between OsiriX, a CE-marked and FDA-approved DICOM viewer, and AI-driven tools deployed in any supported programming language (e.g., Python). OsiriXgrpc's design provides users with a unified platform to query, interact with, and visualise AI-generated outputs directly within OsiriX. To demonstrate its potential, we developed an AI Co-pilot for radiology that leverages OsiriXgrpc for iterative "request-to-answer" interactions between users and GenAI models, allowing real-time data queries and AI-generated output visualisation within the same DICOM viewer. We have adapted OsiriXgrpc to allow users to: (i) interrogate Foundation Large-Language Models (LLMs) to generate text from text-based prompts, (ii) employ Foundation Vision-Language Models (VLMs) to generate text by combining text and image prompts, and (iii) employ a one-click Foundation AI-driven segmentation model to generate Regions of Interest (ROIs) by combining points/bounding boxes with text prompts. For this proof-of-concept report, we applied OpenAI's LLMs and VLMs for text generation and the Segment Anything Model (SAM) for generating ROIs. We provide evidence for successful implementation of the plug-in, including visualisation of the AI-generated outputs for each model tested. We hypothesise that OsiriXgrpc can lower adoption barriers, facilitating GenAI models integration into clinical trials and routine healthcare, even in resource-limited settings, including low/middle income countries (LMICs).
Indexed as
Identifiers
41102424What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.