AI has the potential to transform healthcare delivery yet there is an urgent need for governance processes to guide the safe, fair, and secure adoption of AI in clinical settings. Stanford HAI’s multidisciplinary Healthcare AI Policy Steering Committee conducts research and convenes discussions to develop tangible recommendations for policymakers.
Stanford HAI’s Healthcare AI Policy Steering Committee is a multidisciplinary committee of Stanford faculty and scholars that are working together to advance the governance of healthcare AI. The group brings together academics, physicians, lawyers, computer scientists, and ethicists who believe deeply in both the power of AI applications to transform healthcare delivery and the urgent need to review existing regulatory frameworks to ensure these tools are safe, fair, and secure for clinical use.
By conducting interdisciplinary, evidence-based research and convening multi-stakeholder discussions, the committee aims to develop tangible recommendations for policymakers that help ensure healthcare AI can benefit patients, doctors, and developers alike.
Every year — as part of Stanford’s Health AI Week — the committee convenes a closed-door policy workshop to discuss policy challenges with a select group of leading policymakers, scientists, healthcare providers, ethicists, AI developers, and patient advocates.
In May 2024, participants discussed gaps in federal policy related to AI software for clinical decision support, healthcare enterprise AI tools, and patient-facing AI.
In June 2025, the focus was on patient perspectives as participants explored policy challenges in AI-related payment policy, insurance decisions, and patient trust.
In June 2026, the discussion focused on emerging efforts to regulate the use of AI in mental health, for therapy and emotional support.
Our committee members publish timely research at the intersection of healthcare, law, policy, ethics, and AI. We highlight a selection of their latest research papers at the bottom of this page.
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This brief demonstrates how real-time monitoring can address critical gaps in the oversight of radiological AI tools.
This brief proposes governance mechanisms for the growing use of AI in health insurance utilization review.
Stanford scholars respond to a federal RFC on evaluating AI-enabled medical devices, recommending policy interventions to help mitigate the harms of AI-powered chatbots used as therapists.
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. 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 January 2026, Utah announced a first-of-its kind pilot program allowing an autonomous artificial intelligence (AI) agent to renew prescriptions for consumers who request it. The state agreed not to enforce its unprofessional conduct laws against the developer, Doctronic, if the company adheres to a contract that includes safety and privacy protections. The pilot program includes 192 drugs for chronic conditions. Although physicians will initially validate the AI’s actions, the pilot program will swiftly become one of the first deployments at scale of an autonomous, agentic system in medicine. The announcement prompted concern from associations of physicians and pharmacists who opined that AI “should NOT be making care decisions.”
In January 2026, Utah announced a first-of-its kind pilot program allowing an autonomous artificial intelligence (AI) agent to renew prescriptions for consumers who request it. The state agreed not to enforce its unprofessional conduct laws against the developer, Doctronic, if the company adheres to a contract that includes safety and privacy protections. The pilot program includes 192 drugs for chronic conditions. Although physicians will initially validate the AI’s actions, the pilot program will swiftly become one of the first deployments at scale of an autonomous, agentic system in medicine. The announcement prompted concern from associations of physicians and pharmacists who opined that AI “should NOT be making care decisions.”
Health insurers and health care provider organizations are increasingly using artificial intelligence (AI) tools in prior authorization and claims processes. AI offers many potential benefits, but its adoption has raised concerns about the role of the “humans in the loop,” users’ understanding of AI, opacity of algorithmic determinations, underperformance in certain tasks, automation bias, and unintended social consequences. To date, institutional governance by insurers and providers has not fully met the challenge of ensuring responsible use. However, several steps could be taken to help realize the benefits of AI use while minimizing risks. Drawing on empirical work on AI use and our own ethical assessments of provider-facing tools as part of the AI governance process at Stanford Health Care, we examine why utilization review has attracted so much AI innovation and why it is challenging to ensure responsible use of AI. We conclude with several steps that could be taken to help realize the benefits of AI use while minimizing risks.
Health insurers and health care provider organizations are increasingly using artificial intelligence (AI) tools in prior authorization and claims processes. AI offers many potential benefits, but its adoption has raised concerns about the role of the “humans in the loop,” users’ understanding of AI, opacity of algorithmic determinations, underperformance in certain tasks, automation bias, and unintended social consequences. To date, institutional governance by insurers and providers has not fully met the challenge of ensuring responsible use. However, several steps could be taken to help realize the benefits of AI use while minimizing risks. Drawing on empirical work on AI use and our own ethical assessments of provider-facing tools as part of the AI governance process at Stanford Health Care, we examine why utilization review has attracted so much AI innovation and why it is challenging to ensure responsible use of AI. We conclude with several steps that could be taken to help realize the benefits of AI use while minimizing risks.
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.
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.
Permeation of artificial intelligence (AI) tools into health care tests traditional understandings of what patients should be told about their care. Despite the general importance of informed consent, decision support tools (eg, automatic electrocardiogram readers, rule-based risk classifiers, and UpToDate summaries) are not usually discussed with patients even though they affect treatment decisions. Should AI tools be treated similarly? The legal doctrine of informed consent requires disclosing information that is material to a reasonable patient’s decision to accept a health care service, and evidence suggests that many patients would think differently about care if they knew it was guided by AI. In recent surveys, 60% of US adults said they would be uncomfortable with their physician relying on AI,1 70% to 80% had low expectations AI would improve important aspects of their care,2 only one-third trusted health care systems to use AI responsibly,3 and 63% said it was very true that they would want to be notified about use of AI in their care.
Permeation of artificial intelligence (AI) tools into health care tests traditional understandings of what patients should be told about their care. Despite the general importance of informed consent, decision support tools (eg, automatic electrocardiogram readers, rule-based risk classifiers, and UpToDate summaries) are not usually discussed with patients even though they affect treatment decisions. Should AI tools be treated similarly? The legal doctrine of informed consent requires disclosing information that is material to a reasonable patient’s decision to accept a health care service, and evidence suggests that many patients would think differently about care if they knew it was guided by AI. In recent surveys, 60% of US adults said they would be uncomfortable with their physician relying on AI,1 70% to 80% had low expectations AI would improve important aspects of their care,2 only one-third trusted health care systems to use AI responsibly,3 and 63% said it was very true that they would want to be notified about use of AI in their care.
Policymakers, academics, healthcare providers, AI developers, and patient advocates convened by Stanford HAI identify critical gaps in how we regulate AI tools used for therapy and emotional support.
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