A general-purpose AI assistant embedded in an open-source radiology
information system
- URL: http://arxiv.org/abs/2303.10338v1
- Date: Sat, 18 Mar 2023 05:27:43 GMT
- Title: A general-purpose AI assistant embedded in an open-source radiology
information system
- Authors: Saptarshi Purkayastha, Rohan Isaac, Sharon Anthony, Shikhar Shukla,
Elizabeth A. Krupinski, Joshua A. Danish, and Judy W. Gichoya
- Abstract summary: We describe the novel Human-AI partnership capabilities of the platform, including few-shot learning and swarm learning approaches.
We developed an active learning strategy within the RIS, so that the human radiologist can enable/disable AI annotations as well as "fix"/relabel the AI annotations.
This helps establish a partnership between the radiologist user and a user-specific AI model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Radiology AI models have made significant progress in near-human performance
or surpassing it. However, AI model's partnership with human radiologist
remains an unexplored challenge due to the lack of health information
standards, contextual and workflow differences, and data labeling variations.
To overcome these challenges, we integrated an AI model service that uses DICOM
standard SR annotations into the OHIF viewer in the open-source LibreHealth
Radiology Information Systems (RIS). In this paper, we describe the novel
Human-AI partnership capabilities of the platform, including few-shot learning
and swarm learning approaches to retrain the AI models continuously. Building
on the concept of machine teaching, we developed an active learning strategy
within the RIS, so that the human radiologist can enable/disable AI annotations
as well as "fix"/relabel the AI annotations. These annotations are then used to
retrain the models. This helps establish a partnership between the radiologist
user and a user-specific AI model. The weights of these user-specific models
are then finally shared between multiple models in a swarm learning approach.
Related papers
- Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey [49.29751866761522]
This paper aims to investigate the intersection of GenAI and SAR.
First, we illustrate the common data generation-based applications in SAR field.
Then, an overview of the latest GenAI models is systematically reviewed.
Finally, the corresponding applications in SAR domain are also included.
arXiv Detail & Related papers (2024-11-05T03:06:00Z) - AI-Aided Kalman Filters [65.35350122917914]
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing.
Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering.
This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
arXiv Detail & Related papers (2024-10-16T06:47:53Z) - Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks [55.15079732226397]
Embodied AI is a rapidly advancing field that bridges the gap between cyberspace and physical space.
In VEANET, embodied AI twins act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving.
arXiv Detail & Related papers (2024-10-02T02:20:42Z) - Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs [18.025481751074214]
We introduce a system, named ReXKG, which extracts structured information from processed reports to construct a radiology knowledge graph.
We conduct an in-depth comparative analysis of AI-generated and human-written radiology reports, assessing the performance of both specialist and generalist models.
arXiv Detail & Related papers (2024-08-26T16:28:56Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - Unmasking Biases and Navigating Pitfalls in the Ophthalmic Artificial
Intelligence Lifecycle: A Review [3.1929071422400446]
This review article breaks down the AI lifecycle into seven steps.
Data collection; defining the model task; data pre-processing and labeling; model development; model evaluation and validation; deployment.
Finally, post-deployment evaluation, monitoring, and system recalibration and delves into the risks for harm at each step and strategies for mitigating them.
arXiv Detail & Related papers (2023-10-08T03:49:42Z) - Current State of Community-Driven Radiological AI Deployment in Medical
Imaging [1.474525456020066]
This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium.
We identify barriers between AI-model development in research labs and subsequent clinical deployment.
We discuss various AI integration points in a clinical Radiology workflow.
arXiv Detail & Related papers (2022-12-29T05:17:59Z) - Self-supervised Multi-modal Training from Uncurated Image and Reports
Enables Zero-shot Oversight Artificial Intelligence in Radiology [31.045221580446963]
We present a model dubbed Medical Cross-attention Vision-Language model (Medical X-VL)
Our model enables various zero-shot tasks for oversight AI, ranging from the zero-shot classification to zero-shot error correction.
Our method was especially successful in the data-limited setting, suggesting the potential widespread applicability in medical domain.
arXiv Detail & Related papers (2022-08-10T04:35:58Z) - Choose, not Hoard: Information-to-Model Matching for Artificial
Intelligence in O-RAN [8.52291735627073]
Open Radio Access Network (O-RAN) is an emerging paradigm, whereby network infrastructure elements communicate via open, standardized interfaces.
A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller.
In this paper we introduce, discuss, and evaluate the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training.
arXiv Detail & Related papers (2022-08-01T15:24:27Z) - DIME: Fine-grained Interpretations of Multimodal Models via Disentangled
Local Explanations [119.1953397679783]
We focus on advancing the state-of-the-art in interpreting multimodal models.
Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
arXiv Detail & Related papers (2022-03-03T20:52:47Z) - Data-Driven and SE-assisted AI Model Signal-Awareness Enhancement and
Introspection [61.571331422347875]
We propose a data-driven approach to enhance models' signal-awareness.
We combine the SE concept of code complexity with the AI technique of curriculum learning.
We achieve up to 4.8x improvement in model signal awareness.
arXiv Detail & Related papers (2021-11-10T17:58:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.