All Work Published on Healthcare
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.
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.
Russ Altman’s Testimony Before the U.S. Senate Committee on Health, Education, Labor, and Pensions
In this testimony presented to the U.S. Senate Committee on Health, Education, Labor, and Pensions hearing titled “AI’s Potential to Support Patients, Workers, Children, and Families,” Russ Altman highlights opportunities for congressional support to make AI applications for patient care and drug discovery stronger, safer, and human-centered.
In this testimony presented to the U.S. Senate Committee on Health, Education, Labor, and Pensions hearing titled “AI’s Potential to Support Patients, Workers, Children, and Families,” Russ Altman highlights opportunities for congressional support to make AI applications for patient care and drug discovery stronger, safer, and human-centered.
Readers wanted to know if their therapy chatbot could be trusted, whether their boss was automating the wrong job, and if their private conversations were training tomorrow's models.
Readers wanted to know if their therapy chatbot could be trusted, whether their boss was automating the wrong job, and if their private conversations were training tomorrow's models.
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.
Michelle M. Mello's Testimony Before the U.S. House Committee on Energy and Commerce Health Subcommittee
In this testimony presented to the U.S. House Committee on Energy and Commerce’s Subcommittee on Health hearing titled “Examining Opportunities to Advance American Health Care through the Use of Artificial Intelligence Technologies,” Michelle M. Mello calls for policy changes that will promote effective integration of AI tools into healthcare by strengthening trust.
In this testimony presented to the U.S. House Committee on Energy and Commerce’s Subcommittee on Health hearing titled “Examining Opportunities to Advance American Health Care through the Use of Artificial Intelligence Technologies,” Michelle M. Mello calls for policy changes that will promote effective integration of AI tools into healthcare by strengthening trust.
