Healthcare
AI systems are driving enhanced patient care and optimized clinical decisions. Monitoring these systems ensures reliability, compliance, and patient safety.
Compliance and Regulation Management
Protecting patient privacy and ensuring ethical data use are critical responsibilities for healthcare providers. Monitoring allows providers to identify bias and fairness, track and audit the use of data, monitor model performance, and identify potential breaches or misuse of sensitive information.
Patient Care and Safety
AI powers diagnosis, treatment planning, and decision-making. But when errors or biases occur, it can have serious consequences. By proactively monitoring, providers can catch errors in real-time, minimizing the risk of harm to patients and ensuring that care is delivered safely and efficiently.
Cost and Resource Optimization
The advantages of AI-driven cost optimizations for retail staffing, operations, and supply chain management are enormous. However, data that powers these models degrade over time resulting in incorrect cost and resource predictions which if left unnoticed, can result in financial losses.
Ensure reliable and responsible AI-powered experiences across the business
Major healthcare providers rely on WhyLabs to power AI-system monitoring to ensure ML models are accurate, reliable, and meet patient safety standards.
Get real-time insights into all AI-powered decisions
Ensure patient safety and improve the quality of care
Catch and fix issues before they impact the business or patient care
Monitor AI applications without compromising data privacy
As healthcare companies increasingly rely on AI to support clinical decisions and optimize processes, there is a growing risk of bad data leading to inaccurate predictions or recommendations. To ensure the reliability and accuracy of AI-powered systems, companies need to have visibility into the health of their AI models and the quality of the data used to train and update them.
Providers can ensure that they are providing the best possible care to their patients and complying with regulations, all while benefiting from cost and resource efficiencies, by monitoring the performance of ML models over time. WhyLabs offers a privacy-preserving architecture that does not involve data duplication, making it an ideal solution for ML applications in highly regulated industries like Healthcare.
"We chose WhyLabs as an observability capability for our ML Platform because of the ease of integration and rich capabilities that enable us to meet Model Health Equity Governance guidelines and minimize time-to-insight across model operation tasks."
— Engineering Director
Major Healthcare Provider
