Artificial Intelligence and Health

Our faculty are examining the benefits and potential pitfalls of using Artificial Intelligence (AI) in health care and policy. They are investigating biases behind algorithms and the accuracy and transparency of machine learning, while studying ethical issues that arise as AI tools are integrated into hospital care, and exploring legal and regulatory strategies to protect patient safety, data privacy, and patients' and health-care practitioners' interests.

JAMA Report Calls for Stronger Oversight of Health Care AI

SHP's Michelle Mello joins a global group of 65 thought leaders from academia, technology companies, regulatory agencies, and health systems dissected and debated actionable solutions to effectively & safely deploy AI into clinical practice.

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Michelle Mello testifies before House Committee on Energy and Commerce

Use of AI in American Health Care

Michelle Mello testifies before the U.S. House Committee on Energy & Commerce about opportunities to advance American health care through the use of AI technologies.

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House Energy & Commerce Health Subcommittee on AI in U.S. HealthcareStanford Health Policy's Michelle Mello testifies in hearing about the opportunities to advance American health care through the use of AI technologies.

Stanford Health Policy's Alyce Adams addresses AI conference

Legal Risks and Rewards of Artificial Intelligence in Health Care

During Stanford Health AI Week, Stanford Health Policy researchers address issues of liability risk and the ethical use of AI in health care, making the case for tools that address liability and risk—while making patient safety and concerns a priority.

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Barbed wire from correctional center and surrounding community

AI Outperforms Traditional Methods in Controlling Disease Spread Between Prisons and Communities

SHP researchers led by Jeremy Goldhaber-Fiebert, PhD, built a model to test whether AI could beat traditional methods to stop disease spread between prisons and nearby communities. The verdict was clear.

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Policy Brief: Increasing Fairness in Medicare Payment Algorithms

This policy brief focuses on research by Marissa Reitsma and Sherri Rose, which introduces two new algorithms that can promote fairer Medicare Advantage spending for minority populations.

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Applying Machine-Learning Approaches to Antibiotic Resistance

Jonathan Chen and Mary K. Goldstein are using data science and machine learning to help doctors and health-care facilities make better informed decisions to combat antimicrobial resistance.

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Health Equity and AISherri Rose explains the importance of equity and fairness when using AI and machine learning tools to develop health-care platforms.

SHP Faculty Policy Briefs with Stanford Institute for Human-Centered Artificial Intelligence

Increasing Fairness in Medicare Payment Algorithms

The Complexities of Race Adjustment in Health Algorithms

Understanding Liability Risk from Healthcare AI

Balancing Fairness and Efficiency in Health Plan Payments

Ethical Obligations to Inform Patients About Use of AI Tools

To help health care leaders and clinicians navigate the thorny terrain of using artificial intelligence (AI) tools in their testing and care, SHP's Michelle Mello and colleagues provide a framework for deciding what patients should be told about AI tools.

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Legal Risks and Rewards of Artificial Intelligence in Health Care

Stanford Health Policy researchers address issues of liability risk and the ethical use of AI in health care, making the case for tools that address liability and risk—while making patient safety and concerns a priority.

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Integrating AI into Qualitative Analysis for Health Services Research

This AcademyHealth blog post by SHP's Sara Singer and colleagues explores the use of AI to enhance qualitative analysis for HSR, including challenges, questions for consideration, and assessing utility while models are still improving.

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Regulation of Health and Health Care Artificial Intelligence

In this JAMA Viewpoint, SHP's Michelle Mello discusses the paucity of formal regulations dealing with artificial intelligence in health care and what may lie ahead.

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How AI Diagnostic Tools Can Lead to Financial Burdens for Patients

According to the co-authors of a perspective published in the New England Journal of Medicine, the use of AI diagnostic tools by medical practitioners can lead to unexpected financial burdens for their patients.

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Policy Brief: The Complexities of Race Adjustment in Health Algorithms

As policymakers, health-care practitioners, and technologists pursue the application of AI and machine learning (ML) algorithms in health care, this policy brief underscores the need for health equity research and highlights the limitations of employing technical “fixes” to address deep-seated health inequities.

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Developing Ethics in AI

Michelle Mello and colleagues are using award from the Patient-Centered Outcomes Research Institute (PCORI) to build a practical, patient-centered method for ethical review of AI tools.

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TRIPOD+AI: Updated Reporting Guidelines for Clinical Prediction Models

Sherri Rose joins global network of health experts to improve the transparency and accuracy of prediction algorithms.

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Removing Race Adjustment in Chronic Kidney Disease Care

A new study led by Stanford Health Policy researchers finds that algorithmic changes to a chronic kidney disease care equation are likely insufficient to achieve health equity as many other structural inequities remain.

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The Safe Inclusion of Pediatric Data in AI-Driven Medical Research

AI algorithms often are trained on adult data, which can skew results when evaluating children. A perspective piece by SHP's Sherri Rose and several Stanford Medicine colleagues lays out an approach for pediatric populations.

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Using Artificial Intelligence Tools and Health Insurance Coverage Decisions

It would seem like AI would be a logical tool to help evaluate insurance coverage and claims. But results so far have been sobering, leading to class-action lawsuits and congressional committees demanding answers.

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Exploring Liability Risks of Using AI Tools in Patient Care

SHP's Michelle Mello and co-author analyzed more than 800 tort cases involving both AI and conventional software in health care and non-health-care contexts to see how decisions related to AI and liability might play out in the courts.

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AI Alone Will Not Reduce the Administrative Burden of Health Care

In this JAMA Network Viewpoint, Stanford Health Policy's Kevin Schulman and Perry Nielsen Jr. look at the impact Large Language Models could have on our complex health-care billing system.

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