Grants and Contributions

The module will use an AI vision agent to intelligently capture patient information directly from third-party applications, eliminating manual data re-entry, reducing transcription errors, and enabling richer, more accurate documentation and data extraction.

Build a hyper automation platform using data science, artificial intelligence (AI), and machine learning (ML) techniques to hyperscale firm’s unique business model and expand by unlocking the predictive and learning capabilities of its data.

The Project will support pilot testing of AI strategies, determine target architecture and quantitative technical objectives for implementation of an AI solution that supports the natural language description of a query to a reporting database.

This project aims to leverage Artificial Intelligence (AI) as an enabling technology to redefine business-to-business (B2B) ecosystem development for the logistics management industry.

This project aims to create an AI Asissted software environment for creating economic models using Limestone's proprietary unified cost-benefit analysis (UCBA) paradigm and automatically calculate the results and export the model and results in different formats (e.g., Excel, Word, PDF) for client submission.

This project to experiment and resolve key design and technical challenges in AI Knowledge Graph orchestration, multi-system integration, and intelligent agent design.

Intelligent engine for automatic real-time incident detection and optimized resource allocation in a mass transit environment.

This project represents a comprehensive artificial intelligence initiative that will be strategically integrated across the entire IBRA product and service platform, significantly enhancing functionality, accuracy, and market value.

The purpose of this project is to develop automated, deep learning-based imaging biomarkers that predict outcomes of breast cancer patients with solid tumors based on various modalities of medical imaging.
By enabling an artificial neural network to “learn” what visual features in thousands of imaging studies are associated with respective patients’ actual future outcomes, the resulting deep learning algorithm will be able to predict survival for a new patient’s imaging study.

During the project, Clinisys intends to integrate and test mature Generative AI tools such as scribe via API integration to AI-enable the firm's flagship product – Clinisys EMR. The firm will develop AI scribe specific interfaces that would allow relevant voice data through this interface to be transcribed using AI into text. The intended users will be physicians and clinicians using the system that would allow recording of patient interviews and population of relevant patient encounter fields from within Clinisys EMR interface. This will shorten the time of encounter where currently either the physicians must type the notes or use pre-configured patient encounter templates.