About

Melissa Creary, PhD, MPH, is an Associate Professor of Health Management and Policy and of Global Public Health at the University of Michigan School of Public Health. She is an interdisciplinary social scientist with expertise spanning public health, science and technology studies, medical anthropology, and bioethics. With over 20 years of experience working with the sickle cell community as a scientist, policy maker, and public health researcher, her primary research interests include how science, culture, and policy intersect, particularly around ethical, legal, and social concerns (ELSI). Her work critically analyzes the embodiment of policy via genetic and racial identities and explores the bioethical implications of policies related to race and science. Creary's research provides important insights into the development of shared language for global research agendas, policy development, patient diagnosis, and healthcare provision. She is especially interested in the simultaneous constructions of race and science through policy and how issues of inclusion and knowledge production are at odds with structural and societal barriers. Her conceptual work includes the development of the idea of 'bounded justice,' which critiques how public health and technological policies often fail to address underlying structural and historical injustices, thereby inhibiting justice even in well-meaning programs. She also coined the concept of 'biocultural citizenship,' examining how biology intersects with cultural identity and influences notions of citizenship, trust, and community perspectives in the United States. Additionally, Creary centers anti-racism and health equity in her research, mentoring, and administrative roles, including leading projects aimed at developing institutional interventions in public health higher education and local health departments. Her empirical research often uses sickle cell disease as a lens to investigate health equity and health policy, shaping her broader conceptual work around justice and citizenship.

Research topics

• Medicine
• Computer Science
• Social psychology
• Chromatography
• Nursing
• Environmental health
• Psychology
• Biochemistry
• Chemistry
• Pathology
• Gerontology

Selected publications

• Cultivating Authentic Partnership: Cooperative Development of a Toolkit to Mitigate Group Harm in Biorepository‐Enabled Research

  Health Expectations · 2026-03-18

  articleOpen access

  INTRODUCTION: Practitioners currently lack guidance on how to evaluate the potential negative impacts of biorepository research on communities. We set out to create a product for researchers and oversight boards that would assist them in 'doing right' by communities as they conduct this research. Given that this product would represent the interests of affected communities, it was essential that community voices led its development. One well-documented challenge of community-engaged research is navigating the systemic power imbalance between communities and research institutions. This paper details our participatory research (PR) approach, which sought to mitigate these dynamics through extension of the Community Engagement Studio (CES) methodology for sustained, multi-year engagement with the same community experts and facilitators. METHODS: Our adapted participatory methodology was developed and iterated through a three-phase project. In Phase 1: Community-Driven Conceptualisation, we convened seven 90-min CESs (stylised focus groups) with four cohorts of community experts and facilitators from marginalised groups: American Indian, Alaska Native, Native Hawaiian, Pacific Islander; Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual, Two-Spirit; Black/African American; and individuals with high-penetrance genetic variants. In Phase 2: Community-Centred Design and Feedback, the community experts and facilitators reviewed the initial toolkit design generated from CES-derived insights in six 1-hour meetings. Finally, in Phase 3: Community Co-Analysis, the community experts and facilitators co-analysed toolkit pilot feedback in a series of three to five 90-min sessions across four pilot sites. Throughout each phase, community feedback was used to drive planning, finalise materials and define takeaways. RESULTS: Our sustained engagement promoted trust and enabled deep exploration of complex topics. Challenges included retaining community experts over time and bridging conceptual insights with concrete design. CONCLUSION: This paper offers a reference for optimising research impact through effective PR, highlighting the benefits of sustained engagement and strategic mitigation of power imbalances in academic-community collaborations. PATIENT OR PUBLIC CONTRIBUTION: Members of select communities affected by group harm were included as compensated collaborators in each study phase. These community experts drove early ideation, informed beta design, co-analysed pilot data and provided integrated feedback on this manuscript.

  PublisherDOI

• A Collaborative Approach to Enhance Capacity and Power for Community-driven Policy Change: Project HEARD

  Progress in community health partnerships · 2025-09-01 · 1 citations

  articleOpen access

  BACKGROUND: A longstanding community-based participa-tory research center designed Project Health Equity via Advocacy for Resources in Detroit (HEARD) to enhance the capacity, collective power, and impact of community-based organizations-working in partnership with academics-to advance policy change for health equity in their communities. OBJECTIVES: We describe how Project HEARD supported community-academic teams to develop policy advocacy campaigns that included 1-year goals for equity-focused change. METHODS: Project HEARD had the following main components: a cohort of community-academic teams, policy change workshops, policy advocacy grant, mentoring by community-academic pairs, and online strategy sessions. LESSONS LEARNED: Supporting community-driven policy change requires recognizing and building on teams' contexts, history, and expertise; tailoring support for teams with diverse policy experiences; and identifying additional ways to support sustainability. CONCLUSIONS: Project HEARD's approach and initial lessons learned can inform projects in diverse contexts aiming to amplify community-led policy change to support health equity.

  PublisherOA PDFDOI

• The racial politics of visibility and equity in genome-editing therapies for sickle cell disease

  Social Science & Medicine · 2025-08-05 · 2 citations

  articleSenior author
  PublisherDOI

• Why Building Power Is Key to Protecting Academic Public Health and Advancing Health Equity

  American Journal of Public Health · 2025-10-08 · 3 citations

  editorialOpen access
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• COVID-19 Immunization Coverage Among People With Sickle Cell Disease

  JAMA Network Open · 2024-01-08 · 4 citations

  articleOpen access

  This cross-sectional study compares the completion of the primary COVID-19 vaccine series in Michigan residents with vs without sickle cell disease and by age group.

  PublisherOA PDFDOI

• Indicators for engaging communities

  2024-06-27

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• “Resilience?” perspectives from adults living with sickle cell disease

  Journal of the National Medical Association · 2024-03-01

  article
  PublisherDOI

• Racism is an ethical issue for healthcare artificial intelligence

  Cell Reports Medicine · 2024-06-01 · 11 citations

  articleOpen access

  There is growing attention and evidence that healthcare AI is vulnerable to racial bias. Despite the renewed attention to racism in the United States, racism is often disconnected from the literature on ethical AI. Addressing racism as an ethical issue will facilitate the development of trustworthy and responsible healthcare AI. There is growing attention and evidence that healthcare AI is vulnerable to racial bias. Despite the renewed attention to racism in the United States, racism is often disconnected from the literature on ethical AI. Addressing racism as an ethical issue will facilitate the development of trustworthy and responsible healthcare AI. Artificial intelligence (AI) tools are becoming more widely used in healthcare. They are used for both administrative tasks, such as predicting appointment no-shows, and clinical tasks, such as identifying cancerous tissue in medical images or predicting the onset of sepsis. Although AI has many beneficial uses, there is growing attention and evidence that AI used for healthcare is vulnerable to problems that have been observed in other fields, such as racial bias and gender bias.1Benjamin R. Race after technology: Abolitionist tools for the new Jim code. Polity, 2019Google Scholar,2McKay C. Predicting risk in criminal procedure: Actuarial tools, algorithms, AI and judicial decision-making.Curr. Issues Crim. Justice. 2019; 32: 22-39https://doi.org/10.1080/10345329.2019.1658694Crossref Scopus (33) Google Scholar Bias in AI can emerge at different stages of the development and implementation process, including conceptualization, design, data collection and processing, training, and implementation, and can have harmful effects, often on already marginalized groups.3Chen I.Y. Pierson E. Rose S. Joshi S. Ferryman K. Ghassemi M. Ethical Machine Learning in Healthcare.Annu. Rev. Biomed. Data Sci. 2021; 4: 123-144Crossref PubMed Google Scholar In response to these issues, there has been a focus not only on identifying problematic scenarios to which AI contributes but also on defining what ethical AI in healthcare could be. The attention to ethical AI, both in healthcare and in other sectors, has developed contemporaneously with increasing attention in the United States to racism, particularly anti-Black racism. However, despite this parallel focus on ethical AI and renewed attention to racism in the United States, addressing racism in healthcare and developing ethical AI are largely disconnected efforts. We assert that this is, in part, because racism is not often framed as an ethical issue in bioethics and other ethics discourses.4Ray K. Black Bioethics and How the Failures of the Profession Paved the Way for Its Existence.Bioethics Today. 2020; (August 6, 2020)https://bioethicstoday.org/blog/black-bioethics-and-how-the-failures-of-the-profession-paved-the-way-for-its-existence/?fbclid=IwAR224R1cNKi5kSAGK-T94dA_oD6-bXD-A4nL6o2PY4UBT3Gg3TcmGn7VaH4Google Scholar Second, we assert that more attention to and recognition of racism as an ethical issue in healthcare would facilitate the connection of racism to the development of ethical, responsible, and trustworthy healthcare AI. Third, we believe that viewing racism as an ethical issue can make efforts to minimize the harms and increase the benefits of healthcare AI throughout the development life cycle more effective. Considerations of racism should be central to health AI ethics because racism is an injustice, and justice is a central issue in moral philosophy overall and in bioethics specifically. Recently, in response to increased attention to the killing of Black individuals by law enforcement in the United States, bioethics scholars have called for increasing focus on racism as a justice and bioethics issue, since racism has profound effects on the health and well-being of Black people and other racially oppressed people.5Mithani Z. Cooper J. Boyd J.W. Race, Power, and COVID-19: A Call for Advocacy within Bioethics.Am. J. Bioeth. 2021; 21: 11-18Crossref Scopus (47) Google Scholar The disproportionate COVID-19 incidence, complications, and mortality in Black and Brown communities in the United States sparked the Centers for Disease Control and Prevention to name racism a threat to public health, though scholars have argued this for many years.6Jones C.P. Invited commentary: “race,” racism, and the practice of epidemiology.Am. J. Epidemiol. 2001; 154: 299-306Crossref PubMed Scopus (395) Google Scholar Obermeyer’s widely cited example of a healthcare algorithm favoring healthier White patients over sicker Black patients for additional healthcare resources has been held up as an example of algorithmic bias.7Obermeyer Z. Powers B. Vogeli C. Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations.Science. 2019; 366: 447-453Crossref PubMed Scopus (2160) Google Scholar However, when racism is seen as an ethical issue, this same example is more than just “bias,” but a case of racial injustice. The data for this healthcare algorithm show that Black patients suffer the injustice of lower healthcare expenditures despite worse health and then the algorithm compounds this by deprioritizing their healthcare resource needs. In addition, when racism is considered alongside other more familiar ethical issues like privacy and consent, it adds additional dimensions for consideration in discussions about trustworthy or responsible AI. For example, in the increasing attention on how to make healthcare AI more trustworthy and responsible, issues like transparency, privacy, and security are important to consider. However, a consideration of racism alongside trustworthiness would prompt additional inquiries about how structural racism and/or interpersonal racism might affect efforts to make algorithms more trustworthy. For example, there is some literature on the importance of bringing together a diverse range of stakeholders to help shape the development of ethical, trustworthy, and responsible AI.8Siala H. Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: A systematic review.Soc. Sci. Med. 2022; 296114782Crossref PubMed Scopus (67) Google Scholar However, without explicit attention to racism, these efforts to give stakeholders, particularly racialized minority groups, a “seat at the table” could result in “bounded justice,9Creary M.S. Bounded Justice and the Limits of Health Equity.J. Law Med. Ethics. 2021; 49: 241-256Crossref PubMed Scopus (37) Google Scholar” as Creary argues. In these cases, efforts at inclusion do not fully recognize the historical and current marginalizations that can limit full participation and eventual just outcomes, even when racialized minority groups are invited to participate in stakeholder engagement. The attention to the harms in AI, including when used in healthcare, is important. However, the focus on the dangers of biased healthcare is often framed as a largely technical issue that can be solved by getting better data. However, AI bias is a result of sociotechnical processes, and purely technical fixes will not be sufficient.10Ferryman K. Mackintosh M. Ghassemi M. Considering Biased Data as Informative Artifacts in AI-Assisted Health Care.N. Engl. J. Med. 2023; 389: 833-838Crossref PubMed Scopus (14) Google Scholar It is important to recognize the “ordinariness of racism.11Ford C.L. Griffith D.M. Bruce M.A. Gilbert K.L. Racism: Science & Tools for the Public Health Professional. American Public Health Association, 2019Crossref Google Scholar” That is, the ubiquity of racism, not its absence, characterizes society’s normal state and is not always perceptible. This ordinariness is baked into technology like AI.12Ford C.L. Airhihenbuwa C.O. The public health critical race methodology: praxis for antiracism research.Soc. Sci. Med. 2010; 71: 1390-1398Crossref PubMed Scopus (467) Google Scholar Recently, the White House’s Office of Science and Technology Policy released the Blueprint for an AI Bill of Rights, which names algorithmic discrimination, rather than simply AI bias, as a key problem to address. This is notable because the term algorithmic discrimination brings our attention to the social harms that result from algorithmic bias. Often these harms parallel already existing forms of social exclusion and marginalization, such as racism. Though many publications focusing on ethics in healthcare AI focus on bias, they make sparse mention of racism specifically, which privileges technical expertise and implies that mathematical fairness solutions are all that is needed. We believe that racism should be viewed as an ethical issue in healthcare AI because specifically calling out and focusing on racism provides an opportunity to show how racism can affect other aspects of ethical healthcare AI, not just algorithmic bias. Calling out racism in AI can bring focus to historical and current racial health disparities, such as differences in healthcare access, screening, testing, and treatment, which all influence biased data. Focusing on racism in AI as an ethical issue brings additional and important factors into the frame of consideration and opens opportunities to bring providers, funders, and other nonanalytic experts with an interest in advancing medical AI work into the conversation. We do not suggest that writing on AI healthcare ethics should merely start including the term “racism.” Instead, we would like to see scholarship on healthcare AI and ethics include explanations of how training data become biased. In other words, scholarship should describe how racism happens in our social world, and in healthcare specifically, and how those practices become imprinted onto data that are used for healthcare AI. For example, racism is embedded in healthcare and public health systems in different ways—from race-based clinical algorithms to clinical interactions—that affect data recorded in healthcare settings.13Vyas D.A. Eisenstein L.G. Jones D.S. Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms.N. Engl. J. Med. 2020; 383: 874-882https://doi.org/10.1056/nejmms2004740Crossref PubMed Scopus (0) Google Scholar Such scholarship would require expertise from a diversity of scholars, including social scientists, bioethicists, and historians, who are not always included in AI development decisions. Developing such scholarship might also require novel empirical approaches and praxes based on antiracism, equity, and justice frameworks. Funders interested in building more equitable healthcare AI algorithms should also invest in interdisciplinary initiatives focused on understanding and redressing racial bias in the AI development pipeline. More fulsome discussions of racism in the literature on the ethics of healthcare AI would also include more specificity on the ways that racism impacts clinical care. We also advocate for healthcare AI ethics to include more clear distinctions between structural racism and individual racist practices and/or beliefs and how these might affect healthcare AI through the life cycle—from dataset creation to design and development to implementation. In addition, we advocate for more in-depth discussions in the healthcare AI literature on how racism operates differently between groups, such as by discussing how anti-Black racism might be different from other kinds of racism and understanding the nuance of intersectionality.14Crenshaw K.W. Mapping the margins: Intersectionality, identity politics, and violence against women of color.in: Fineman M.A. Mykitiuk R. The Public Nature of Private Violence. Routledge, 2013: 93-118Google Scholar Finally, in a recent commentary, colleagues have considered the different ways the informatics field should recognize institutional, systemic, and structural racism and proposed the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms.15Platt J. Nong P. Merid B. Raj M. Cope E. Kardia S. Creary M. Applying anti-racist approaches to informatics: a new lens on traditional frames.J. Am. Med. Inform. Assoc. 2023; 30: 1747-1753https://doi.org/10.1093/jamia/ocad123Crossref Scopus (1) Google Scholar We expand on this argument by noting that the field of healthcare AI ethics would also be augmented by discussions of antiracism. Intentional antiracist efforts can catalyze new sociotechnical practices for healthcare AI development and governance. Antiracism is defined as “[a] commitment to dismantling racism, which has dimension that are institutional and social as well as attitudinal and behavioral.”11Ford C.L. Griffith D.M. Bruce M.A. Gilbert K.L. Racism: Science & Tools for the Public Health Professional. American Public Health Association, 2019Crossref Google Scholar By centering on antiracism, literature addressing AI in healthcare can help contextualize and provide a deeper understanding of the policies and practices that perpetuate racist ideas and actions in medical care. The fast-moving practice of AI offers informaticists, scholars, and researchers an opportunity to create frameworks that meaningfully engage an intersectional approach to ethics and antiracism and that are critically reflective of field norms and standards. These conversations provide directions forward for questions raised by the recognition of racist diagnoses or clinical interactions and may catalyze conversation about structural changes to improve equitable use of AI in medicine. Additionally, they can provide the groundwork for procedural standards that ensure developers, funders, and others committed to advancing medical AI are transparent in addressing how racism may shape the resources and outcomes of their work. We recommend that future AI health ethics frameworks should (1) explicitly discuss how systemic and individual racism creates biased data and algorithms, (2) discuss solutions to address racial bias that are grounded in approaches that have proven to be effective, (3) discuss how proposed ethical frameworks can benefit communities or individuals impacted by racial inequities, and (4) make ethical recommendations that are intentionally antiracist. K.F. and E.O.N. contributed to the conceptualization of the manuscript. N.C., K.F., M.C., and E.O.N. contributed to manuscript development, literature review, analysis, drafting, and editing. K.F. is a member of the All of Us Research Program’s institutional review board and a member of the digital ethics advisory board of Merck KGaA. E.O.N. is part of the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program team at the National Institutes of Health through the Intergovernmental Personnel Act (IPA) Mobility Program.

  PublisherOA PDFDOI

• Health Equity Requires Working Toward Antiracist Local Public Health Departments

  Public Health Reports · 2024-03-22 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Building the foundation for a community-generated national research blueprint for inherited bleeding disorders: research priorities in health services; diversity, equity, and inclusion; and implementation science

  Expert Review of Hematology · 2023-03-15 · 23 citations

  articleOpen access

  BACKGROUND: The National Hemophilia Foundation (NHF) conducted extensive all-stakeholder inherited bleeding disorder (BD) community consultations to inform a blueprint for future research. Sustaining and expanding the specialized and comprehensive Hemophilia Treatment Center care model, to better serve all people with inherited BDs (PWIBD), and increasing equitable access to optimal health emerged as top priorities. RESEARCH DESIGN AND METHODS: NHF, with the American Thrombosis and Hemostasis Network (ATHN), convened multidisciplinary expert working groups (WG) to distill priority research initiatives from consultation findings. WG5 was charged with prioritizing health services research (HSR); diversity, equity, and inclusion (DEI); and implementation science (IS) research initiatives to advance community-identified priorities. RESULTS: WG5 identified multiple priority research themes and initiatives essential to capitalizing on this potential. Formative studies using qualitative and mixed methods approaches should be conducted to characterize issues and meaningfully investigate interventions. Investment in HSR, DEI and IS education, training, and workforce development are vital. CONCLUSIONS: An enormous amount of work is required in the areas of HSR, DEI, and IS, which have received inadequate attention in inherited BDs. This research has great potential to evolve the experiences of PWIBD, deliver transformational community-based care, and advance health equity.

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Frequent coauthors

• Paul J. Fleming

  University of Michigan–Ann Arbor

  13 shared

• Ella Greene‐Moton

  9 shared

• Kent Key

  Flint Community Schools

  9 shared

• Barbara A. Israel

  University of Michigan–Ann Arbor

  9 shared

• Lisa Cacari Stone

  9 shared

• Ángela Reyes

  University of California, Riverside

  9 shared

• Amy J. Schulz

  University of Michigan–Ann Arbor

  9 shared

• Nina Wallerstein

  University of New Mexico

  9 shared