Advancing health equity with artificial intelligence

Thomasian, Nicole M; Eickhoff, Carsten; Adashi, Eli Y.

doi:10.1057/s41271-021-00319-5

Cited by 43 publications

(31 citation statements)

References 35 publications

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“…It is critical for policy-makers to understand that bias mitigation should not end with AI model development but, rather, extend across the product lifecycle. We believe in line with Thomasian et al 53…”

Section: Important Considerationssupporting

confidence: 92%

“…The largest concern surrounding AI solutions is the potential for systems to continue perpetuating inequities. [6][7][8]11,12,39,40 Thus, AI initiatives should have two main goals: (1) they should be designed and utilized in a manner that does not create or maintain health disparities currently experienced by vulnerable groups, and (2) they should address and remove existing health disparities. 6,39 To ensure that all healthcare-based AI embodies these two goals, it is important to create system level changes such as a federal and/or provincial regulatory framework that oversees the equity dimensions in the implementation of AI solutions.…”

Section: Equity Assessmentmentioning

confidence: 99%

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Equity within AI systems: What can health leaders expect?

Gurevich

Hassan

Morr

2022

Healthc Manage Forum

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Artificial Intelligence (AI) for health has a great potential; it has already proven to be successful in enhancing patient outcomes, facilitating professional work and benefiting administration. However, AI presents challenges related to health equity defined as an opportunity for people to reach their fullest health potential. This paper discusses the opportunities and challenges that AI presents in health and examines ways in which inequities related to AI can be mitigated.

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Section: Important Considerationssupporting

confidence: 92%

Section: Equity Assessmentmentioning

confidence: 99%

Equity within AI systems: What can health leaders expect?

Gurevich

Hassan

Morr

2022

Healthc Manage Forum

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“…AI has been criticized for being “no more than human behaviour reflected back to us” [ 98 ]. Inherent to this argument is the ability of AI to “reflect the biases present in our collective conscience” [ 70 ]. The discourse on guidelines to rectify and prioritize health equity in the development life cycle of an AI system [ 99 ], as well as around ethical AI applications generally, is becoming more prolific.…”

Section: Discussionmentioning

confidence: 99%

“…Lucidchart allowed non–computing science team members (eg, psychiatry and lived experience experts) to communicate necessary chatbot conversational flow behaviors to the computing science team clearly and effectively, including any emergency- or urgency-related prompts and responses. The open-source use of big data has been significantly criticized for perpetuating systemic racism and societal inequalities [ 70 ]. As such, developing training data using our multidisciplinary team was important to ensure that chatbot behavior remained respectful and reduce existing issues inherent in big data.…”

Section: Methodsmentioning

confidence: 99%

Developing, Implementing, and Evaluating an Artificial Intelligence–Guided Mental Health Resource Navigation Chatbot for Health Care Workers and Their Families During and Following the COVID-19 Pandemic: Protocol for a Cross-sectional Study

Noble¹,

Zamani²,

Gharaat³

et al. 2022

JMIR Res Protoc

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Background Approximately 1 in 3 Canadians will experience an addiction or mental health challenge at some point in their lifetime. Unfortunately, there are multiple barriers to accessing mental health care, including system fragmentation, episodic care, long wait times, and insufficient support for health system navigation. In addition, stigma may further reduce an individual’s likelihood of seeking support. Digital technologies present new and exciting opportunities to bridge significant gaps in mental health care service provision, reduce barriers pertaining to stigma, and improve health outcomes for patients and mental health system integration and efficiency. Chatbots (ie, software systems that use artificial intelligence to carry out conversations with people) may be explored to support those in need of information or access to services and present the opportunity to address gaps in traditional, fragmented, or episodic mental health system structures on demand with personalized attention. The recent COVID-19 pandemic has exacerbated even further the need for mental health support among Canadians and called attention to the inefficiencies of our system. As health care workers and their families are at an even greater risk of mental illness and psychological distress during the COVID-19 pandemic, this technology will be first piloted with the goal of supporting this vulnerable group. Objective This pilot study seeks to evaluate the effectiveness of the Mental Health Intelligent Information Resource Assistant in supporting health care workers and their families in the Canadian provinces of Alberta and Nova Scotia with the provision of appropriate information on mental health issues, services, and programs based on personalized needs. Methods The effectiveness of the technology will be assessed via voluntary follow-up surveys and an analysis of client interactions and engagement with the chatbot. Client satisfaction with the chatbot will also be assessed. Results This project was initiated on April 1, 2021. Ethics approval was granted on August 12, 2021, by the University of Alberta Health Research Board (PRO00109148) and on April 21, 2022, by the Nova Scotia Health Authority Research Ethics Board (1027474). Data collection is anticipated to take place from May 2, 2022, to May 2, 2023. Publication of preliminary results will be sought in spring or summer 2022, with a more comprehensive evaluation completed by spring 2023 following the collection of a larger data set. Conclusions Our findings can be incorporated into public policy and planning around mental health system navigation by Canadian mental health care providers—from large public health authorities to small community-based, not-for-profit organizations. This may serve to support the development of an additional touch point, or point of entry, for individuals to access the appropriate services or care when they need them, wherever they are. International Registered Report Identifier (IRRID) PRR1-10.2196/33717

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“…The use of artificial intelligence (AI) in clinical care and public health contexts has expanded rapidly in recent years [1][2][3][4][5][6], including throughout the COVID-19 pandemic [7][8][9][10][11][12][13][14][15]. While emerging AI applications have the potential to improve health care quality and fairness [16][17][18][19][20][21], they may alternatively perpetuate or exacerbate inequities if they are not designed, deployed, and monitored appropriately [22][23][24][25][26].…”

Section: Background and Rationalementioning

confidence: 99%

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Berdahl¹,

Baker²,

Mann³

et al. 2023

JMIR AI

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Background Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. Objective This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. Methods We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. Results In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. Conclusions Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

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Advancing health equity with artificial intelligence

Cited by 43 publications

References 35 publications

Equity within AI systems: What can health leaders expect?

Equity within AI systems: What can health leaders expect?

Developing, Implementing, and Evaluating an Artificial Intelligence–Guided Mental Health Resource Navigation Chatbot for Health Care Workers and Their Families During and Following the COVID-19 Pandemic: Protocol for a Cross-sectional Study

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

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