A fairer way forward for AI in health care

Nordling, Linda

doi:10.1038/d41586-019-02872-2

Cited by 73 publications

(42 citation statements)

References 5 publications

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“…healthcare costs) which did not provide the full picture regarding the health of black people [61]. Another example came from the University of Chicago hospital system, where researchers found that if postal codes had been used in their machine-learning algorithm to optimize hospital resources, resources available for black people would have been diverted towards "wealthy white people, exacerbating existing biases in the system" [62]. As residential segregation is also a known consequence of structural racism [63], this shows how structural racism can have many repercussions on algorithmic decisions in the healthcare system.…”

Section: Second Node: Integration Of Biased Health Data For Pm Initiamentioning

confidence: 99%

Structural racism in precision medicine: leaving no one behind

et al. 2020

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Background: Precision medicine (PM) is an emerging approach to individualized care. It aims to help physicians better comprehend and predict the needs of their patients while effectively adopting in a timely manner the most suitable treatment by promoting the sharing of health data and the implementation of learning healthcare systems. Alongside its promises, PM also entails the risk of exacerbating healthcare inequalities, in particular between ethnoracial groups. One often-neglected underlying reason why this might happen is the impact of structural racism on PM initiatives. Raising awareness as to how structural racism can influence PM initiatives is paramount to avoid that PM ends up reproducing the pre-existing health inequalities between different ethnoracial groups and contributing to the loss of trust in healthcare by minority groups. Main body: We analyse three nodes of a process flow where structural racism can affect PM's implementation. These are: (i) the collection of biased health data during the initial encounter of minority groups with the healthcare system and researchers, (ii) the integration of biased health data for minority groups in PM initiatives and (iii) the influence of structural racism on the deliverables of PM initiatives for minority groups. We underscore that underappreciation of structural racism by stakeholders involved in the PM ecosystem can be at odds with the ambition of ensuring social and racial justice. Potential specific actions related to the analysed nodes are then formulated to help ensure that PM truly adheres to the goal of leaving no one behind, as endorsed by member states of the United Nations for the 2030 Agenda for Sustainable Development. Conclusion: Structural racism has been entrenched in our societies for centuries and it would be naïve to believe that its impacts will not spill over in the era of PM. PM initiatives need to pay special attention to the discriminatory and harmful impacts that structural racism could have on minority groups involved in their respective projects. It is only by acknowledging and discussing the existence of implicit racial biases and trust issues in healthcare and research domains that proper interventions to remedy them can be implemented.

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Section: Second Node: Integration Of Biased Health Data For Pm Initiamentioning

confidence: 99%

Structural racism in precision medicine: leaving no one behind

et al. 2020

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“…Social justice Concerns about how these new technologies might affect the distribution of the benefits and burdens related to AI use in healthcare [3,9,16,17,19,[23][24][25][26][27][28][29].…”

Section: Parental Concerns Descriptionmentioning

confidence: 99%

Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare

et al. 2020

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Precision medicine relies upon artificial intelligence (AI)-driven technologies that raise ethical and practical concerns. In this study, we developed and validated a measure of parental openness and concerns with AI-driven technologies in their child’s healthcare. In this cross-sectional survey, we enrolled parents of children <18 years in 2 rounds for exploratory (n = 418) and confirmatory (n = 386) factor analysis. We developed a 12-item measure of parental openness to AI-driven technologies, and a 33-item measure identifying concerns that parents found important when considering these technologies. We also evaluated associations between openness and attitudes, beliefs, personality traits, and demographics. Parents (N = 804) reported mean openness to AI-driven technologies of M = 3.4/5, SD = 0.9. We identified seven concerns that parents considered important when evaluating these technologies: quality/accuracy, privacy, shared decision making, convenience, cost, human element of care, and social justice. In multivariable linear regression, parental openness was positively associated with quality (beta = 0.23), convenience (beta = 0.16), and cost (beta = 0.11), as well as faith in technology (beta = 0.23) and trust in health information systems (beta = 0.12). Parental openness was negatively associated with the perceived importance of shared decision making (beta = −0.16) and being female (beta = −0.12). Developers might support parental openness by addressing these concerns during the development and implementation of novel AI-driven technologies.

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“…Organizational excellence is defined as an effort to develop different processes and standards intended to engage employees to deliver excellent products [62]. Consequently, developers are authorized to update AI models without to review by the FDA [63]. However, a testing process must crucially be developed so that the updated models' performance is not below that of the primary models [64].…”

Section: Resultsmentioning

confidence: 99%

“…From China's perspective, AI is a powerful tool for economic success, military dominance, and controlling the population [72]. Chinese companies accumulate a tremendous amount of health-related data, which can be used in AI development, owing to lenient regulation on data collection and little public concern about data privacy [63,75]. However, in recent years, the Chinese public has started to petition large companies, such as Baidu and Alibaba, for the right to data privacy [75].…”

Section: Discussionmentioning

confidence: 99%

“…Encouraging patients to share their electronic medical record information and medical images, and creating datasets of volunteers' data have been described in several studies [83]. For example, in 2015, the U.S. National Institutes of Health set an objective to develop genomic data, lifestyle data, and biomarker data from 1 million volunteers from diverse backgrounds [63]. Another project supported by Google is developing a dataset comprising data from 10,000 volunteers over 4 years [98].…”

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confidence: 99%

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A Safety Controlling System Framework for Implementing Artificial Intelligence in Healthcare

Davahli¹,

Karwowski²,

Fiok³

et al. 2020

Preprint

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In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to solve the AI black-box mystery in the healthcare industry. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first-level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

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A fairer way forward for AI in health care

Abstract: Artificial intelligence has great potential to improve health care, but without careful implementation, only a privileged portion of society will benefit.

Cited by 73 publications

References 5 publications

Structural racism in precision medicine: leaving no one behind

Structural racism in precision medicine: leaving no one behind

Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare

A Safety Controlling System Framework for Implementing Artificial Intelligence in Healthcare

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