All Work Published on Regulation, Policy, Governance
Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations
Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data. This review highlights the potential of conditional generative models (CGMs) to create patient-specific synthetic data for a variety of precision medicine applications. We survey CGM approaches that tackle two medical applications: correcting for data representation biases and simulating digital health twins. We additionally explore how the surveyed methods handle modeling tabular medical data and briefly discuss evaluation criteria. Finally, we summarize the technical, medical, and ethical challenges that must be addressed before CGMs can be effectively and safely deployed in the medical field.
Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data. This review highlights the potential of conditional generative models (CGMs) to create patient-specific synthetic data for a variety of precision medicine applications. We survey CGM approaches that tackle two medical applications: correcting for data representation biases and simulating digital health twins. We additionally explore how the surveyed methods handle modeling tabular medical data and briefly discuss evaluation criteria. Finally, we summarize the technical, medical, and ethical challenges that must be addressed before CGMs can be effectively and safely deployed in the medical field.
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.
Elon Musk was forced to put restrictions on X and its AI chatbot, Grok, after its image generator sparked outrage around the world. Grok created non-consensual sexualized images, prompting some countries to ban the bot. Liz Landers discussed Grok's troubles with Riana Pfefferkorn of the Stanford Institute for Human-Centered Artificial Intelligence.
Elon Musk was forced to put restrictions on X and its AI chatbot, Grok, after its image generator sparked outrage around the world. Grok created non-consensual sexualized images, prompting some countries to ban the bot. Liz Landers discussed Grok's troubles with Riana Pfefferkorn of the Stanford Institute for Human-Centered Artificial Intelligence.
Response to the Department of Education’s Request for Information on AI in Education
Stanford scholars respond to a federal RFI on advancing AI in education, urging policymakers to anchor their approach in proven research.
Stanford scholars respond to a federal RFI on advancing AI in education, urging policymakers to anchor their approach in proven research.
Scholars develop a framework in collaboration with luxury goods multinational LVMH that lays out how large companies can flexibly deploy principles on the responsible use of AI across business units worldwide.
Scholars develop a framework in collaboration with luxury goods multinational LVMH that lays out how large companies can flexibly deploy principles on the responsible use of AI across business units worldwide.
This brief evaluates the impact of authorship labels on the persuasiveness of AI-written policy messages.
This brief evaluates the impact of authorship labels on the persuasiveness of AI-written policy messages.
