Aidoc Unveils BRIDGE Framework for Safe Implementation of Clinical AI Systems
In a significant move towards enhancing the deployment of artificial intelligence in healthcare, Aidoc has released a pioneering open-source framework called BRIDGE. The announcement was made during the HLTH Europe conference held in Amsterdam. This innovative initiative aims to assist healthcare organizations in integrating AI solutions reliably and effectively while ensuring compliance with regulatory standards. BRIDGE stands for Blueprint for Resilient Integration and Deployment of Guided Excellence and has been developed in collaboration with NVIDIA.
The primary objective of BRIDGE is to offer health systems, clinicians, and technology leaders a structured approach to navigate the complex landscape of clinical AI adoption. This framework has been meticulously developed through the contributions of 17 leading organizations, including esteemed institutions like the University of Washington and Ochsner Health. The collaborative effort brings together insights from a diverse range of stakeholders, from academic researchers to clinical practitioners who are at the forefront of AI implementation.
As AI applications continue to gain traction in hospital settings, establishing a clear framework becomes essential to facilitate consistent and trusted implementations across various healthcare systems. The BRIDGE framework outlines critical criteria needed for AI solutions to be deemed healthcare-ready. This includes guidelines on technical integration, validation processes, regulatory checkpoints, and trust-building mechanisms that promote transparency and explainability in AI outputs.
"To safely deploy AI in healthcare, we need more than strong algorithms. We need shared structure," emphasized Reut Yalon, PhD, Chief Product Officer at Aidoc. "BRIDGE provides that structure, helping the industry align on what constitutes a successful implementation so we can accelerate adoption without compromising safety or operational performance."
The framework is structured around four primary areas:
1. Model Versus Full Solution: BRIDGE stresses the importance of distinguishing between AI models and comprehensive solutions. This involves the necessary infrastructure, user experience, and workflow integration that extend beyond simplistic algorithm functionalities.
2. Minimum Viable Production Environment (MVPE): This element addresses the essential technical conditions, such as validation and cost benchmarks, that any AI solution must fulfill prior to being deployed in clinical settings.
3. Trust-Building Mechanisms: To cultivate user trust, BRIDGE encapsulates requirements for transparency, explainability, and the defensibility of outcomes across varied clinical applications.
4. Scalability Guidelines: This area focuses on ensuring interoperability, sustainable integration across multiple AI models, and long-term performance monitoring, reflecting the evolving nature of healthcare demands.
The ultimate goal of the BRIDGE framework is to provide a unified structure that can guide CIOs, governance leaders, and platform vendors in evaluating and deploying AI solutions effectively. It sets a community-aligned standard that is not just a vendor specification but rather a dynamic framework that can adapt as clinical AI technologies advance.
"Deploying AI at scale requires more than technical efficiency; it necessitates a strong foundation built on trust, transparency, and readiness at the system level," asserted Efstathia Andrikopoulou, MD, an echocardiography medical director at Harborview Medical Center. "BRIDGE lays out those standards in an actionable manner, which can greatly benefit health systems as they implement AI solutions responsibly."
As the landscape of clinical AI continues to evolve, BRIDGE will adapt to incorporate new technologies and regulatory information, ensuring its relevance in guiding healthcare practitioners and machine learning developers. The collaborative spirit surrounding its creation promises ongoing input from industry leaders, meaning the framework can grow and improve over time.
Notably, BRIDGE is available to the public at no cost via the website www.aibridgeframework.com. Healthcare organizations interested in exploring this framework further, contributing to its development, or learning more about AI deployment can access the resources provided and engage with the growing community focused on innovative healthcare solutions.
About Aidoc:
Aidoc has positioned itself as a leader in delivering clinical AI solutions that enhance diagnostic accuracy and improve patient outcomes. Their innovative aiOS™ platform integrates pivotal insights directly into clinical workflows, ensuring healthcare providers can react promptly to patients’ needs. With FDA-cleared AI solutions operational in over 1,500 hospitals, Aidoc is setting a new benchmark for efficiency, reliability, and confidence within the healthcare sector.
The primary objective of BRIDGE is to offer health systems, clinicians, and technology leaders a structured approach to navigate the complex landscape of clinical AI adoption. This framework has been meticulously developed through the contributions of 17 leading organizations, including esteemed institutions like the University of Washington and Ochsner Health. The collaborative effort brings together insights from a diverse range of stakeholders, from academic researchers to clinical practitioners who are at the forefront of AI implementation.
As AI applications continue to gain traction in hospital settings, establishing a clear framework becomes essential to facilitate consistent and trusted implementations across various healthcare systems. The BRIDGE framework outlines critical criteria needed for AI solutions to be deemed healthcare-ready. This includes guidelines on technical integration, validation processes, regulatory checkpoints, and trust-building mechanisms that promote transparency and explainability in AI outputs.
"To safely deploy AI in healthcare, we need more than strong algorithms. We need shared structure," emphasized Reut Yalon, PhD, Chief Product Officer at Aidoc. "BRIDGE provides that structure, helping the industry align on what constitutes a successful implementation so we can accelerate adoption without compromising safety or operational performance."
The framework is structured around four primary areas:
1. Model Versus Full Solution: BRIDGE stresses the importance of distinguishing between AI models and comprehensive solutions. This involves the necessary infrastructure, user experience, and workflow integration that extend beyond simplistic algorithm functionalities.
2. Minimum Viable Production Environment (MVPE): This element addresses the essential technical conditions, such as validation and cost benchmarks, that any AI solution must fulfill prior to being deployed in clinical settings.
3. Trust-Building Mechanisms: To cultivate user trust, BRIDGE encapsulates requirements for transparency, explainability, and the defensibility of outcomes across varied clinical applications.
4. Scalability Guidelines: This area focuses on ensuring interoperability, sustainable integration across multiple AI models, and long-term performance monitoring, reflecting the evolving nature of healthcare demands.
The ultimate goal of the BRIDGE framework is to provide a unified structure that can guide CIOs, governance leaders, and platform vendors in evaluating and deploying AI solutions effectively. It sets a community-aligned standard that is not just a vendor specification but rather a dynamic framework that can adapt as clinical AI technologies advance.
"Deploying AI at scale requires more than technical efficiency; it necessitates a strong foundation built on trust, transparency, and readiness at the system level," asserted Efstathia Andrikopoulou, MD, an echocardiography medical director at Harborview Medical Center. "BRIDGE lays out those standards in an actionable manner, which can greatly benefit health systems as they implement AI solutions responsibly."
As the landscape of clinical AI continues to evolve, BRIDGE will adapt to incorporate new technologies and regulatory information, ensuring its relevance in guiding healthcare practitioners and machine learning developers. The collaborative spirit surrounding its creation promises ongoing input from industry leaders, meaning the framework can grow and improve over time.
Notably, BRIDGE is available to the public at no cost via the website www.aibridgeframework.com. Healthcare organizations interested in exploring this framework further, contributing to its development, or learning more about AI deployment can access the resources provided and engage with the growing community focused on innovative healthcare solutions.
About Aidoc:
Aidoc has positioned itself as a leader in delivering clinical AI solutions that enhance diagnostic accuracy and improve patient outcomes. Their innovative aiOS™ platform integrates pivotal insights directly into clinical workflows, ensuring healthcare providers can react promptly to patients’ needs. With FDA-cleared AI solutions operational in over 1,500 hospitals, Aidoc is setting a new benchmark for efficiency, reliability, and confidence within the healthcare sector.
Topics Health）
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