Empowering Ethical, Equitable, and Efficient AI-Driven Healthcare for Everyone
The Trustworthy and Responsible AI Network (TRAINTM) is dedicated to operationalizing trustworthy AI in healthcare. By collaborating with member organizations, TRAIN develops, adopts, and shares best practices to ensure AI is used responsibly and ethically. Our goal is to enable effective, equitable, and efficient AI-driven healthcare for the benefit of all.
Mission
The Trustworthy and Responsible AI Network (TRAINTM) is dedicated to operationalizing responsible AI principles to enhance the quality, safety, and trustworthiness of AI applications in health care.
Who We Are
At the HIMSS 2024 Global Health Conference, a new consortium of healthcare leaders announced the creation of the Trustworthy & Responsible AI Network (TRAINTM), one of the first health AI networks aimed at operationalizing responsible AI principles. Through collaboration, TRAIN members will help improve the quality, safety, and trustworthiness of AI in health by sharing best practices, enabling registration of AI used for clinical care or clinical operations, providing tools to enable measurement of outcomes associated with the implementation of AI, and facilitating the development of a federated national AI outcomes registry for organizations to share amongst themselves.
Values
TRAIN’s initiatives are structured around three core values:
Trustworthiness
Ensuring that AI systems in healthcare are reliable, transparent, and secure. This involves rigorous testing, validation, and continuous monitoring of AI tools to maintain high standards of performance and accountability. Trustworthiness also encompasses building public confidence in AI technologies by making their processes understandable and their outcomes predictable.
Equity
Striving to make the benefits of AI accessible to all healthcare delivery centers, irrespective of their size or resources. This emphasizes the importance of designing AI systems that do not perpetuate or exacerbate existing health disparities. It ensures that AI tools are developed and implemented in ways that protect and promote the welfare of underrepresented or under-resourced populations, and fostering inclusive healthcare improvements.
Research
Advancing the field of AI in healthcare through robust and ethical research practices. This includes conducting interdisciplinary studies that explore the impacts of AI on health outcomes, developing innovative algorithms that address current medical challenges, and ensuring that research findings are disseminated widely to inform policy and practice. Research within TRAIN also focuses on understanding and mitigating potential biases in AI systems and exploring novel ways to integrate AI into clinical workflows effectively.
Goals and Early Objectives
Sharing Best Practices
Disseminating best practices for the use of AI in healthcare, including ensuring the safety, reliability, and monitoring of AI algorithms, as well as developing the necessary skillsets to manage AI responsibly. AI algorithms will not be shared between member organizations or with third parties.
Enabling Registration
Facilitating the registration of AI tools used for clinical care or operations through a secure online portal.
Adopting Measurement Tools
Implementing tools to measure outcomes associated with AI deployment. This includes establishing best practices for evaluating the efficacy and value of AI methods in healthcare settings, leveraging privacy-preserving environments, and considering both pre- and post-deployment stages. A particular focus will be on conducting analyses to assess and mitigate bias.
Facilitating Development of a National Registry
Creating a federated national algorithmovigilance network and an AI practice registry for organizations to share their experiences. This registry will capture real-world outcomes related to the efficacy, safety, and optimization of AI algorithms.
Marketplace Solutions in Development
TRAINTM aims to provide an extensive array of tools, organizations, and services accessible within the Microsoft Platform, catering to diverse needs and requirements in the realm of AI integration and healthcare innovation.
Clinical Testing Labs
Technical Testing Labs
Governance as a Service (GaaS)
Assessment of Organizational Maturity
Consulting
Guiding Principles
Focus on issues and solutions for operationalizing responsible AI, supporting our discussions with evidence wherever possible.
Recognize that priorities may vary based on individual perspectives, and that all viewpoints are important.
Prioritize shared concerns and collaborative efforts that we can best address through TRAINTM, rather than independently.
Acknowledge that resources and capabilities vary across different sites, and focus on approaches that can be applied universally.
Share our findings for the benefit of TRAINTM members and society, while respecting the need for confidentiality in certain areas
From the TRAINTM Founders
"I am excited to partner with my colleagues from our diverse group of health systems and Microsoft in the development and implementation of technologies and capabilities that make health AI more trustworthy. We look forward to leveraging the Coalition for Health AI’s (CHAI) best practice guidelines and guardrails to build practical tools that make responsible AI a reality among healthcare delivery organizations in service to all our patients."
“Even the best healthcare today still suffers from many challenges that AI-driven solutions can substantially improve. However, just as we wouldn’t think of treating patients with a new drug or device without ensuring and monitoring their efficacy and safety, we must test and monitor AI-derived models and algorithms before and after they are deployed across diverse healthcare settings and populations, to help minimize and prevent unintended harms. It is imperative that we work together and share tools and capabilities that enable systematic AI evaluation, surveillance and algorithmvigilance for the safe, effective and equitable use of AI in healthcare. TRAIN is a major step toward that goal.”
"At Advocate Health, innovation is at the core of our drive to advance the science of medicine. As we seek to make care more accessible and affordable for all, address the root causes of health inequities and provide the best health outcomes for our patients, we believe the responsible application of AI and leveraging key partnerships in this space will be essential as we re-imagine how care delivery can be improved in the future."
“Advancing AI in healthcare will be well served through a collective initiative such as this one that will allow us to responsibly harness AI potential for the betterment of patient care and outcomes. We look forward to our role in guiding medicine into an exciting new era of discovery.”
“At Northwestern Medicine, we believe AI in healthcare has the power to have a positive and transformative impact in our clinical care settings. We’re excited to collaborate with this consortium to ensure AI in the healthcare setting is deployed responsibly and in the best interest of our patients and caregivers.”
“When it comes to AI’s tremendous capabilities, there is no doubt the technology has the potential to transform healthcare. However, the processes for implementing the technology responsibly are just as vital. By working together, TRAIN members aim to establish best practices for operationalizing responsible AI, helping improve patient outcomes and safety while fostering trust in healthcare AI.”
