Project CANAIRI
What is Project CANAIRI?

Testing an algorithm in 鈥榮ilent mode鈥 is a period of algorithmic evaluation where the artificial intelligence (AI) tool is running on live or near-live data, making inferences about patients in real-time. These predictions are evaluated for accuracy and provide a test of the model鈥檚 performance in its intended clinical setting but without yet affecting patient care or institutional operations. Many believe it is a critical step to provide key assurances for the responsible and efficient integration of machine learning (ML) tools into clinical and administrative settings.
CANAIRI, or the Collaboration for trANslational AI tRIals (CANAIRI) project, will develop consensus-based standards of best practices and key capabilities for conducting clinical trials. The group advocates for a widening of these 鈥榮ilent鈥 trials toward one that is sociotechnical in nature and wants to draw attention to the importance of this critical testing stage for AI in healthcare.
As a first step, the group uses the term 鈥榯ranslational trial鈥 to widen the scope and practices of silent trials to include human factors, implementation science, operational/systems integration, social license, legal and ethical, economics, environmental, and regulatory considerations. It calls these 鈥榯ranslational trials鈥 to emphasise that when technology is translated from bench to bedside, it needs to be tested to ensure that it is as closely mapped as possible to what the bedside looks like and what the needs are.
Project CANAIRI will undertake an international consensus-generating methodology to identify current practices for translational/silent evaluations, develop guidance for health settings, and generate knowledge products for a variety of stakeholders. This project is the first of its kind and looks to fill a critical gap in current AI translation frameworks.

(Image: Fred Jacobs)
History and collaborators
CANAIRI is spearheaded by AIML Deputy Director, Dr who helped develop the first ethical framework for AI translation. The framework included a silent testing phase and focused on patient outcomes with other CANAIRI members in 2020.
鈥淭he genesis of this idea really came about when I first started my postdoc [research] with Anna Goldenberg,鈥 said Dr McCradden. is the Varma Family Chair in Biomedical Informatics and Artificial Intelligence at The Hospital for Sick Children (SickKids).
鈥淪he was the first to publicise the importance of silent trials [1] and I was really fascinated by this idea, because there really is no equivalent to it in traditional research,鈥 said Dr McCradden.
鈥淢y experience at has shown me that the silent trial is the 'algorithm graveyard. If instead we have an interim testing phase to vet the algorithms that won't work, we can focus translation efforts on those that will work in a basic sense in the real clinical setting,鈥 she continued.
鈥淪o, building out the requirements for this phase is critical to managing expectations and controlling hype in AI so that we don't end up with a collapse of AI optimism down the line.鈥
Among the group鈥檚 members and co-authors are AIML Senior Research Fellow Dr Lauren Oakden-Rayner, 杏吧直播 of Adelaide Professor , AIML Professor , and PhD student Lana Tikhomirov. CANAIRI is also partnering with the Aboriginal Health Unit at [in Adelaide] to ensure that their voices continue to guide CANAIRI鈥檚 work towards an outcome that is ethically defensible and resonates with the communities who are most impacted by AI.
Relevant AIML news articles
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Footnotes
[1] 鈥淒o no harm: a roadmap for responsible machine learning for health care.鈥 Nature Medicine. 2019.