A fundamental barrier the Healthcare industry faces in adoption of Machine Learning is a lack of trust and compliance for artificial intelligence solutions (AI). Join our exclusive session for an in-depth look at how use ML monitoring and explainability to tackle these complex use cases.

Building a machine learning pipeline can be challenging and time-consuming, particularly with compliance expectations rising. ML systems in Healthcare organizations should not be deploying black box models, particularly as the FDA has recommended that models designed to replace physician decision-making begin to be treated as medical devices.

As a result, machine learning systems will be increasingly subject to rigorous frameworks in order to regulate medical devices. This will drastically increase the amount of regulation and scrutiny they are subject to, from both regulators but also to gain the trust of their patients.

Explainability tools such as Alibi Explain and Alibi Detect, can help the end user understand how ML models came to their conclusions and flag any anomalous data that are reducing accuracy in model predictions. With concerns about bias in ML models, providing the evidence that explains the models conclusion will dramatically increase the adoption of AI technology.

Seldon Deploy Advanced drives deeper insights into model behaviour and bias with productised Explainable Artificial Intelligence (XAI) workflows. Seldon empowers organizations with both local and global explainer methods across a range of data modalities to interpret model predictions for black-box and white-box models. You can finally build trust towards transparency of model decisions for compliance and governance purposes.