HLTH Europe conference on Monday, the Trustworthy & Responsible AI Network (TRAIN) announced its expansion into Europe. TRAIN aims to assist organizations in the region in implementing responsible AI through technology-based safeguards.

The European branch of TRAIN includes Erasmus MC (Netherlands), HUS Helsinki University Hospital (Finland), Sahlgrenska University Hospital (Sweden), Skåne University Hospital (Sweden), Universita Vita-Salute San Raffaele (Italy), and University Medical Center Utrecht (Netherlands), with Microsoft as the technology partner.

Foundation 29, a nonprofit dedicated to empowering patients and transforming healthcare through data-driven initiatives and innovative technologies, has also joined. The network welcomes additional European healthcare organizations interested in joining.

AI technologies have immense potential to transform healthcare globally by improving patient care outcomes, streamlining processes, and reducing costs. However, as AI technology advances, it is crucial to establish robust development and evaluation standards to ensure responsible and effective applications.

TRAIN aims to enhance the quality, safety, and trustworthiness of AI tools in healthcare, ensuring clinicians and patients benefit from these innovations.

TRAIN was initially formed in March 2024 with leading U.S. healthcare organizations. Its operational objectives include:

Providing technology and tools to implement trustworthy and responsible AI principles at scale.

Collaborating with other TRAIN members and stakeholders to ensure that all organizations, including those in low-resource settings, benefit from responsible AI safeguards.

Sharing best practices related to AI use in healthcare, focusing on safety, reliability, monitoring of AI algorithms, and the necessary skillsets for managing AI responsibly. Data and AI algorithms will not be shared between member organizations or with third parties.

Enabling the registration of AI used in clinical care or operations through a secure online portal.

Providing tools to measure outcomes associated with AI implementation, including best practices for evaluating the efficacy and value of AI methods in healthcare. These tools will leverage privacy-preserving environments and consider both pre- and post-deployment settings.

They will also allow analyses in subpopulations to assess bias. Developing a federated AI outcomes registry for organizations to share real-world outcomes related to the efficacy, safety, and optimization of AI algorithms.








Source: prnewswire.com

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