“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. 