Categories: HealthcareNews

CitiusTech Launches Healthcare’s First End-to-End Generative AI Quality & Trust Solution

PRINCETON, N.J.–(BUSINESS WIRE)–#CitiusTechCitiusTech, a leading provider of healthcare technology services and solutions, announced that it has launched an industry-first solution for healthcare organizations to help address the reliability, quality and trust requirements for Generative AI (Gen AI) solutions. The CitiusTech Gen AI Quality & Trust solution will help organizations design, develop, integrate, and monitor quality and facilitate trust in Generative AI applications, providing the confidence needed to adopt and scale Gen AI applications enterprise-wide.


“We are working with many clients on Gen AI solutions. But we find that over 80% of Gen AI proofs of concept and initiatives are delayed due to reliability and compliance concerns, due to lack of trust in Gen AI solutions. This is the pain point we are trying to solve for our customers, and the healthcare industry at large,” said Rajan Kohli, CEO, CitiusTech. “By prioritizing trust, quality, and reliability, we empower our clients to fully embrace Gen AI technologies, gaining potential competitive advantages in the healthcare sector. We are integrating this capability seamlessly into all our solutions and projects, ensuring our clients can confidently benefit from scaling Gen AI powered solutions.”

Our solution offering is a software-based framework coupled with consulting, implementation, and support services. It leverages a state-of-the-art automated design & decision-making framework that will provide pre-packaged measures, automated output validation and monitoring the quality and trustworthiness of Gen AI solutions.

“At present, there are no established technology or platform agnostic solutions that measure quality and trust of healthcare Gen AI solutions, end-to-end. Approaches used in building and evaluating LLMs, and foundation models are useful, but have not been designed for healthcare,” said Sridhar Turaga, SVP – Data and Analytics, CitiusTech. “Our Gen AI Quality & Trust Solution is the first systematic approach in healthcare to quantitatively measure, verify and monitor Gen AI solutions. This solution also synthesizes and builds on work by AI researchers, platform players, industry forums and regulatory bodies. We go beyond pure math or tech and contextualize everything to healthcare context.”

CitiusTech’s Gen AI Quality & Trust Solutions fully integrate into existing MLOps, DataOps and Quality Management Solutions, and anchors to healthcare use cases and outcomes. Organizations can leverage our healthcare-specific repository of 70+ metrics, 25+ methods across 7 dimensions – Accuracy, Calibration, Robustness, Fairness, Bias, Toxicity and Efficiency. Multiple clients and Healthcare innovators have beta-tested this approach, which has helped shape this offering.

About CitiusTech

CitiusTech is a leading provider of digital technology and consulting services to payer, provider, medical technology, and life sciences companies. With over 8,500 healthcare technology professionals worldwide, CitiusTech powers healthcare digital innovation, business transformation and industry-wide convergence for over 140 organizations, through next-generation technologies, solutions, and products. Key focus areas include healthcare interoperability, secure data management, quality and performance analytics, real world data, clinical research optimization, virtual trials, value-based care, patient experience, medical imaging, connected health, payer-provider convergence, care coordination and population health management. CitiusTech’s cutting-edge technology expertise, deep healthcare domain expertise and a strong focus on digital transformation enables healthcare and life sciences companies to reinvent themselves to deliver better outcomes, accelerate growth, drive efficiencies, and ultimately make a meaningful impact to patients. Follow CitiusTech on Twitter or LinkedIn.

Contacts

Saviera Barretto, CitiusTech, Saviera.Barretto@citiustech.com

Staff

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