Racism is an ethical issue for healthcare artificial intelligence.

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.

Bounded Justice, Inclusion, and the Hyper/Invisibility of Race in Precision Medicine.

I take up the call for a more nuanced engagement with race in bioethics by using Creary's analytic of bounded justice and argue that it helps illuminate processes of racialization, or racial formation, specifically Blackness, as a dialectical processes of both invisibility and hyper-visibility. This dialectical view of race provides a lens through which the ethical, legal, and social implications (ELSI) of genetics and genomics field can reflect on fraught issues such as inclusion in genomic and biomedical research. Countering or interrupting racialization in precision medicine can involve asking how marginalized groups are made invisible or hyper-visible in various aspects of the research process. Incorporating these kinds of questions into biomedical research inclusion efforts could lead to potentially powerful engagements with marginalized groups and could provide the opportunity for stakeholders to engage with the ways that racialization can happen in real-time and might undermine good intentions.

A study protocol to explore and implement community-based point-of-care COVID-19 testing for women who use drugs in Baltimore, Maryland: The CARE study.

Women who use drugs (WWUD) experience structural vulnerabilities (e.g., housing, food insecurities) and comorbidities that elevate their susceptibility to more severe COVID-19 symptoms or fatality compared to similarly-aged women who do not use illicit drugs. Testing is a cornerstone of effective COVID prevention, however, entrenched barriers to healthcare utilization means that WWUD may have diminished accessing to COVID testing. The CARE (COVID Action Research Engagement) study first examines predisposing and enabling factors that predict COVID testing uptake over six months (baseline, 3-, and 6-month follow-up) among a cohort of WWUD (N = 250) in Baltimore, Maryland, providing a nuanced and holistic understanding of how to meaningfully engage WWUD in COVID testing. Then, point-of-care COVID testing will be implemented on a mobile outreach van affiliated with a local community-based organization primarily serving WWUD; anonymous surveys of mobile outreach guests (N = 100) will assess feasibility and acceptability of this integrated testing. The study is grounded in the Behavioral Model for Vulnerable Populations and the Theoretical Framework of Acceptability. We hypothesize that point-of-care COVID testing integrated into a low-barrier harm reduction service, such as a mobile outreach program, will be an enabling environment for COVID testing uptake in part by reducing structural impediments to testing and will be highly feasible and acceptable to participants. Strengths, limitations, and plans for results dissemination are discussed.

Ethical Machine Learning in Healthcare.

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.

Addressing health disparities in the Food and Drug Administration's artificial intelligence and machine learning regulatory framework.

The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparities. However, these tools have also been shown to exacerbate inequities faced by marginalized groups. Focusing on health disparities should be part of good machine learning practice and regulatory oversight of software as medical devices. Using the Food and Drug Administration (FDA)'s proposed framework for regulating machine learning tools in medicine, I show that addressing health disparities during the premarket and postmarket stages of review can help anticipate and mitigate group harms.

Discussions of miscarriage and preterm births on Twitter.

BACKGROUND: Experiences typically considered private, such as, miscarriages and preterm births are being discussed publicly on social media and Internet discussion websites. These data can provide timely illustrations of how individuals discuss miscarriages and preterm births, as well as insights into the wellbeing of women who have experienced a miscarriage. OBJECTIVES: To characterise how users discuss the topic of miscarriage and preterm births on Twitter, analyse trends and drivers, and describe the perceived emotional state of women who have experienced a miscarriage. METHODS: We obtained 291 443 Twitter postings on miscarriages and preterm births from January 2017 through December 2018. Latent Dirichlet Allocation (LDA) was used to identify major topics of discussion. We applied time series decomposition methods to assess temporal trends and identify major drivers of discussion. Furthermore, four coders labelled the emotional content of 7282 personal miscarriage disclosure tweets into the following non-mutually exclusive categories: grief/sadness/depression, anger, relief, isolation, annoyance, and neutral. RESULTS: Topics in our data fell into eight groups: celebrity disclosures, Michelle Obama's disclosure, politics, healthcare, preterm births, loss and anxiety, flu vaccine and ectopic pregnancies. Political discussions around miscarriages were largely due to a misunderstanding between abortions and miscarriages. Grief and annoyance were the most commonly expressed emotions within the miscarriage self-disclosures; 50.6% (95% confidence interval [CI] 49.1, 52.2) and 16.2% (95% CI 15.2, 17.3). Postings increased with celebrity disclosures, pharmacists' refusal of prescribed medications and outrage over the high rate of preterm births in the United States. Miscarriage disclosures by celebrities also led to disclosures by women who had similar experiences. CONCLUSIONS: This study suggests that increase in discussions of miscarriage on social media are associated with several factors, including celebrity disclosures. Additionally, there is a misunderstanding of the potential physical, emotional and psychological impacts on individuals who lose a pregnancy due to a miscarriage.

A Culture of Understanding: Reflections and Suggestions from a Genomics Research Community Board.

There has been limited community engagement in the burgeoning field of genomics research. In the wake of a new discovery of genetic variants that increase the risk of kidney failure and are almost unique to people of African ancestry, community and clinical leaders in Harlem, New York, formed a community board to inform the direction of related research. The board advised all aspects of a study to assess the impact of testing for these genetic variants at primary care sites that serve diverse populations, including explaining genetic risk to participants. By reflecting on the board's experiences, we found that community voices can have tangible impact on research that navigates the controversial intersection of race, ancestry, and genomics by heightening vigilance, fostering clear communication between researchers and the community, and encouraging researchers to cede some control. Our reflections and work provide a strong justification for longitudinal community partnerships in genomics research.

Race, Genomics and Chronic Disease: What Patients with African Ancestry Have to Say.

BACKGROUND: Variants of the APOL1 gene increase risk for kidney failure 10-fold, and are nearly exclusively found in people with African ancestry. To translate genomic discoveries into practice, we gathered information about effects and challenges incorporating genetic risk in clinical care. METHODS: An academic-community-clinical team tested 26 adults with self-reported African ancestry for APOL1 variants, conducting in-depth interviews about patients' beliefs and attitudes toward genetic testing- before, immediately, and 30 days after receiving test results. We used constant comparative analysis of interview transcripts to identify themes. RESULTS: Themes included: Knowledge of genetic risk for kidney failure may motivate providers and patients to take hypertension more seriously, rather than inspiring fatalism or anxiety. Having genetic risk for a disease may counter stereotypes of Blacks as non-adherent or low-literate, rather than exacerbate stereotypes. CONCLUSION: Populations most likely to benefit from genomic research can inform strategies for genetic testing and future research.

Motivations, concerns and preferences of personal genome sequencing research participants: Baseline findings from the HealthSeq project.

Whole exome/genome sequencing (WES/WGS) is increasingly offered to ostensibly healthy individuals. Understanding the motivations and concerns of research participants seeking out personal WGS and their preferences regarding return-of-results and data sharing will help optimize protocols for WES/WGS. Baseline interviews including both qualitative and quantitative components were conducted with research participants (n=35) in the HealthSeq project, a longitudinal cohort study of individuals receiving personal WGS results. Data sharing preferences were recorded during informed consent. In the qualitative interview component, the dominant motivations that emerged were obtaining personal disease risk information, satisfying curiosity, contributing to research, self-exploration and interest in ancestry, and the dominant concern was the potential psychological impact of the results. In the quantitative component, 57% endorsed concerns about privacy. Most wanted to receive all personal WGS results (94%) and their raw data (89%); a third (37%) consented to having their data shared to the Database of Genotypes and Phenotypes (dbGaP). Early adopters of personal WGS in the HealthSeq project express a variety of health- and non-health-related motivations. Almost all want all available findings, while also expressing concerns about the psychological impact and privacy of their results.