All Work Published on Regulation, Policy, Governance
Toward Responsible AI in Health Insurance Decision-Making
This brief proposes governance mechanisms for the growing use of AI in health insurance utilization review.
This brief proposes governance mechanisms for the growing use of AI in health insurance utilization review.
"Countries pursue AI sovereignty with four main objectives in mind: cultural autonomy, national security, economic competitiveness, and regulatory oversight," says Juan N. Pava, Stanford HAI Research Fellow.
"Countries pursue AI sovereignty with four main objectives in mind: cultural autonomy, national security, economic competitiveness, and regulatory oversight," says Juan N. Pava, Stanford HAI Research Fellow.
Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM)
Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users to independently distinguish between trustworthy and unreliable AI predictions, which increases cognitive burden, reduces productivity, and potentially leads to misdiagnoses. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black-box commercial AI products, EMM operates independently without requiring access to internal AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM can successfully categorize confidence in the AI-generated prediction, suggest appropriate actions, and help physicians recognize low confidence scenarios, ultimately reducing cognitive burden. Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings.
Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users to independently distinguish between trustworthy and unreliable AI predictions, which increases cognitive burden, reduces productivity, and potentially leads to misdiagnoses. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black-box commercial AI products, EMM operates independently without requiring access to internal AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM can successfully categorize confidence in the AI-generated prediction, suggest appropriate actions, and help physicians recognize low confidence scenarios, ultimately reducing cognitive burden. Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings.
Response to OSTP's Request for Information on Accelerating the American Scientific Enterprise
Stanford scholars respond to a federal RFI on scientific discovery, calling for the government to support a new “team science” academic research model for AI-enabled discovery.
Stanford scholars respond to a federal RFI on scientific discovery, calling for the government to support a new “team science” academic research model for AI-enabled discovery.
Stanford HAI Executive Director Russell Wald discusses world models, saying many policymakers or government officials need to better understand the technologies and their implications.
Stanford HAI Executive Director Russell Wald discusses world models, saying many policymakers or government officials need to better understand the technologies and their implications.
Developing mental health AI tools that improve care across different groups and contexts
In order to realize the potential of mental health AI applications to deliver improved care, a multipronged approach is needed, including representative AI datasets, research practices that reflect and anticipate potential sources of bias, stakeholder engagement, and equitable design practices.
In order to realize the potential of mental health AI applications to deliver improved care, a multipronged approach is needed, including representative AI datasets, research practices that reflect and anticipate potential sources of bias, stakeholder engagement, and equitable design practices.