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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.
This methodological paper presents the Global AI Vibrancy Tool, an interactive suite of visualizations designed to facilitate cross-country comparisons of AI vibrancy across countries, using indicators organized into pillars. The tool offers customizable features that enable users to conduct in-depth country-level comparisons and longitudinal analyses of AI-related metrics.
This methodological paper presents the Global AI Vibrancy Tool, an interactive suite of visualizations designed to facilitate cross-country comparisons of AI vibrancy across countries, using indicators organized into pillars. The tool offers customizable features that enable users to conduct in-depth country-level comparisons and longitudinal analyses of AI-related metrics.
The Institute aims to appoint and support promising researchers through its fellowship programs
This year's Index reveals a widening gap between what AI can do and how prepared we are to manage it. While AI continues its rapid integration into the global economy – with technical capabilities improving, investment accelerating, and adoption spreading – the frameworks needed to govern, evaluate, and understand this technology are falling behind. In a field where data transparency is declining, independent, rigorous measurement has never been more critical. This report provides the most comprehensive analysis of AI's trajectory available.
Learn more about our Faculty Affiliate program. Stanford faculty are encouraged to participate.
View research opportunities across HAI's programs, centers, labs, and initiatives.
This year, affinity group topics include accessibility for individuals with disabilities, artistic creation, education, healthcare, journalism, workforce productivity, and more.
Increasing reliance on language models for legal tasks have led to calls for more information about the likelihood and severity of potential errors. Realizing useful and robust transparency requires an institutional perspective, say the editors of a new special section in PNAS.
With increased use of chatbots in mental health contexts, AI developers now rely on human experts to evaluate AI’s responses for “safety” – but experts rarely agree on what’s safe.
Biomni can analyze mountains of medical data, spot patterns humans might miss, and even design experiments—helping researchers make discoveries faster in the race to cure disease.
New AI tools generate hypotheses, design experiments, and find patterns in data—transforming how scientists make discoveries across every field.
PsychAdapter lets researchers dial in on personality traits, age, and mental health characteristics to generate text that sounds like real individuals, opening the door to training simulations and personalized content.
Designed to support new, ambitious, and speculative ideas with the objective of getting initial results
We welcome proposals for research projects that tackle important challenges and opportunities in this space from either a technical or social science perspective, with findings that can generate policy insights and recommendations.
A cross-disciplinary group of Stanford students explores fresh approaches to human-centered AI.