Artificial Intelligence in Healthcare

As Artificial Intelligence (AI) technology continues to evolve, its impact on healthcare is growing at a rapid pace.

Applications in AI are already creating new ways to diagnose, treat, predict, prevent, and cure disease. AI is also being used to accelerate drug development, minimize the impact of infectious disease outbreaks, design and manage clinical trials, and advance operational efficiencies that can reduce administrative burden on healthcare providers. At the same time, AI can also reinforce biases inherent within healthcare systems and potentially cause harm.

Given its unparalleled capacity to quickly analyze vast amounts of data, AI has the potential to revolutionize healthcare delivery. With this, comes a great need for more interdisciplinary research and collaboration to inform policy and practice that will ensure safe and ethical utilization of artificial intelligence tools.

How U-M researchers are tackling Artificial Intelligence to help transform healthcare

With electronic health records becoming the norm, the U.S. has witnessed a wave of data proliferation making the ability to drive some healthcare decisions more rapidly. What are some of the questions and opportunities for Artificial Intelligence (AI) in healthcare? How can this technology be leveraged effectively and responsibly?

This video series highlights how IHPI researchers are exploring and shaping the many facets of AI application in health and healthcare.

Featured Experts

Cornelius James: AI in healthcare: Clinician education and training
Cornelius James, M.D., of the U-M Medical School's Department of Internal Medicine knows as AI becomes a critical component of health care, it’s imperative that medical education prepares physicians to use the tools in this burgeoning field. He directs an evidence-based curriculum at the U-M Medical School and is researching digital health tools to understand how they will be incorporated into medical practice in the future.

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Rahul Ladhania: AI and the application of machine learning methods in healthcare
Rahul Ladhania, Ph.D., M.Phil., of the U-M School of Public Health works to understand how personalized treatment rules and behavioral interventions have an effects health outcomes. He understands the importance of AI and data that can assist with issues like predicting disease onset or devising personalized health care treatment plans.

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Jodyn Platt: AI and the intersection of informatics and ethics in healthcare
Jodyn Platt, Ph.D., M.P.H., of the U-M Medical School's Department of Learning Health Sciences says patients want to know if AI is being used in their health care and what the implications are. She works to understand patients’ questions about AI in health care so that providers can better meet their needs.

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Nicholson Price: AI in law and the application in health policy
Nicholson Price, Ph.D., J.D., of the U-M Law School and U-M Medical School researches how AI might amplify and worsen disparities (racial/ethnic and others) and the implications for fixing these issues at the policy level.

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Kayte Spector-Bagdady: Eliminating bias in AI data sources
Attorney and medical privacy and ethics expert Kayte Spector Bagdady, J.D., M.B.E., discusses the importance of ensuring that the data sources artificial intelligence models are trained on are representative and will not lead to biased results.

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AI & Digital Health Innovation

AI & Digital Health Innovation (AI & DHI), formerly Precision Health, provides the health data, computing resources, and implementation expertise needed to develop and test AI and digital health research at the University of Michigan (U-M). These core resources and services are available to all U-M researchers. Additionally, AI & DHI provides targeted support to researchers through interactive communities, including e-Health and AI (e-HAIL) and the Michigan Genomics Initiative (MGI). These communities embedded within AI & DHI represent an expansion, unification, and enhancement of interdisciplinary efforts at the intersection of AI and health at U-M.

Learn more about the Research Impact and Emerging Innovations

AI & DHI Core Communities

e-Health and Artificial Intelligence (e-HAIL)

Launched in 2021, e-HAIL is a strategic partnership between Michigan Medicine and the U-M College of Engineering, advancing U-M as a premier hub for eHealth and AI to transform health and healthcare through technology.

e-HAIL is a vibrant, multidisciplinary community of clinicians and methodologists dedicated to advancing healthcare innovation through AI and machine learning. By fostering collaboration, supporting grant development, and building research infrastructure, e-HAIL connects researchers with shared interests to conduct high-impact research on critical healthcare applications.

Michigan Genomics Initiative (MGI)

MGI is a collaborative research effort among physicians, researchers, and patients at Michigan Medicine. It is a community built around shared data and resources that focuses on lowering the barriers of entry and allowing a wide range of clinicians and researchers to become involved in genetics research.

MPrOVE: Enhancing healthcare value through machine learning

The Michigan Program on Value Enhancement (MPrOVE) facilitates collaborations across IHPI and Michigan Medicine to deliver projects that right-size care, improve quality, and enhance the value of healthcare services. One recent example: a machine learning model that quickly and accurately determines the ambulatory surgery center site best suited for individual surgical cases, expediting surgical scheduling efficiency from 7.5 to 3.5 days, and saving approximately 100 hours of clinician time each month previously spent on chart review.

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