On February 15, 2024, in PRINCETON, N.J., CitiusTech, a prominent provider of healthcare technology services and solutions, unveiled an unprecedented solution designed to address the reliability, quality, and trust aspects of Generative AI (Gen AI) solutions in healthcare. The CitiusTech Gen AI Quality & Trust solution offers healthcare organizations the tools to design, develop, integrate, and monitor the quality and trustworthiness of Generative AI applications, enabling them to confidently adopt and scale Gen AI applications across their enterprise.
Rajan Kohli, CEO of CitiusTech, emphasized the importance of trust, quality, and reliability in Gen AI solutions, citing that a significant portion of Gen AI initiatives face delays due to concerns regarding reliability and compliance. By focusing on these aspects, CitiusTech aims to empower clients to embrace Gen AI technologies fully, potentially gaining a competitive edge in the healthcare industry.
The solution comprises a software-based framework accompanied by consulting, implementation, and support services. It utilizes an advanced automated design and decision-making framework to offer pre-packaged measures, automated output validation, and monitoring of the quality and trustworthiness of Gen AI solutions.
Sridhar Turaga, Senior Vice President of Data and Analytics at CitiusTech, highlighted the novelty of the Gen AI Quality & Trust Solution in healthcare, noting that existing approaches for evaluating Generative AI solutions lack specificity for the healthcare sector. CitiusTech’s solution provides a systematic approach to quantitatively measure, verify, and monitor Gen AI solutions, synthesizing insights from AI researchers, platform players, industry forums, and regulatory bodies.
The Gen AI Quality & Trust Solutions seamlessly integrate into existing MLOps, DataOps, and Quality Management Solutions, aligning with healthcare use cases and outcomes. Leveraging a healthcare-specific repository of metrics and methods, organizations can ensure accuracy, calibration, robustness, fairness, bias mitigation, toxicity management, and efficiency optimization across their Gen AI solutions. Multiple clients and healthcare innovators have beta-tested this approach, contributing to its refinement and development.