“Democratizing” artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

Rubeis, Giovanni; Dubbala, Keerthi; Metzler, Ingrid

doi:10.3389/fgene.2022.902542

Cited by 20 publications

(12 citation statements)

References 58 publications

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“…Rubeis et al (2022) conclude that democratisation in the context of AI in healthcare requires defining and envisioning a set of social goods (benefits) as well as deliberative processes and modes of participation to ensure that those affected by AI in healthcare have a say in its development and use. 33 Similar considerations apply for nanoEHS and nanoinformatics, especially in the context of their use in driving progress towards the SDGs.…”

Section: Resultsmentioning

confidence: 94%

The role of FAIR nanosafety data and nanoinformatics in achieving the UN sustainable development goals: the NanoCommons experience

Serrano,

Gheorghe,

Exner

et al. 2024

RSC Sustain.

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Section: Resultsmentioning

confidence: 94%

The role of FAIR nanosafety data and nanoinformatics in achieving the UN sustainable development goals: the NanoCommons experience

Serrano,

Gheorghe,

Exner

et al. 2024

RSC Sustain.

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“…We conducted a narrative literature search to identify relevant values in the context of designing and deploying a dSMI at the workplace (conceptual investigation in VSD; refer to Multimedia Appendix 1 for detailed information on the search [15,31,34,35,44,49,54,58,[60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75]). These values were identified using a 2-step process.…”

Section: Literature Search: Identifying Valuesmentioning

confidence: 99%

“…In terms of trading autonomy in exchange for more health benefits, there was a tendency to give up some control over interactions with a dSMI. Professional points of view emphasize that the health care professional is responsible for always balancing out a patient's autonomy and therapeutic care in the setting of internet-or mobile-supported interventions, although this is challenging [64,109].…”

Section: Autonomymentioning

confidence: 99%

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Kerr¹,

Naegelin²,

Benk³

et al. 2023

J Med Internet Res

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Background Work stress places a heavy economic and disease burden on society. Recent technological advances include digital health interventions for helping employees prevent and manage their stress at work effectively. Although such digital solutions come with an array of ethical risks, especially if they involve biomedical big data, the incorporation of employees’ values in their design and deployment has been widely overlooked. Objective To bridge this gap, we used the value sensitive design (VSD) framework to identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, assess how users comprehend these values, and derive specific requirements for an ethics-informed design of dSMIs. VSD is a theoretically grounded framework that front-loads ethics by accounting for values throughout the design process of a technology. Methods We conducted a literature search to identify relevant values of dSMIs at the workplace. To understand how potential users comprehend these values and derive design requirements, we conducted a web-based study that contained closed and open questions with employees of a Swiss company, allowing both quantitative and qualitative analyses. Results The values health and well-being, privacy, autonomy, accountability, and identity were identified through our literature search. Statistical analysis of 170 responses from the web-based study revealed that the intention to use and perceived usefulness of a dSMI were moderate to high. Employees’ moderate to high health and well-being concerns included worries that a dSMI would not be effective or would even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, whereas concerns regarding autonomy, accountability, and identity were rated lower. Moreover, a personalized dSMI with a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) than a dSMI without a monitoring system. In addition, integrability, user-friendliness, and digital independence emerged as novel values from the qualitative analysis of 85 text responses. Conclusions Although most surveyed employees were willing to use a dSMI at the workplace, there were considerable health and well-being concerns with regard to effectiveness and problem perpetuation. For a minority of employees who value digital independence, a nondigital offer might be more suitable. In terms of the type of dSMI, privacy and accountability concerns must be particularly well addressed if a machine learning-based monitoring component is included. To help mitigate these concerns, we propose specific requirements to support the VSD of a dSMI at the workplace. The results of this work and our research protocol will inform future research on VSD-based interventions and further advance the integration of ethics in digital health.

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“…8 Thanks to the democratization of data science, which has been made possible by factors such as increased computing power, open-source software, and low or no-code programming, ML has become more accessible for adoption in public health practices and research. 9,10 For instance, ML has been widely applied to obesity research, enhancing pattern recognition and outcome prediction over conventional statistical approaches. 11 This study aimed to build ML models to correct self-reported anthropometric measures, including height, weight, and BMI, and estimate obesity prevalence in US adults.…”

mentioning

confidence: 99%

“…Machine learning (ML) involves developing and implementing algorithms and models to learn patterns and insights from data without being explicitly programmed 8. Thanks to the democratization of data science, which has been made possible by factors such as increased computing power, open-source software, and low or no-code programming, ML has become more accessible for adoption in public health practices and research 9,10. For instance, ML has been widely applied to obesity research, enhancing pattern recognition and outcome prediction over conventional statistical approaches 11…”

mentioning

confidence: 99%

Building Machine Learning Models to Correct Self-Reported Anthropometric Measures

2023

Journal of Public Health Management and Practice

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Monitoring population obesity risk primarily depends on self-reported anthropometric data prone to recall error and bias. This study developed machine learning (ML) models to correct self-reported height and weight and estimate obesity prevalence in US adults. Individual-level data from 50 274 adults were retrieved from the National Health and Nutrition Examination Survey (NHANES) 1999-2020 waves. Large, statistically significant differences between self-reported and objectively measured anthropometric data were present. Using their self-reported counterparts, we applied 9 ML models to predict objectively measured height, weight, and body mass index. Model performances were assessed using root-mean-square error. Adopting the best performing models reduced the discrepancy between self-reported and objectively measured sample average height by 22.08%, weight by 2.02%, body mass index by 11.14%, and obesity prevalence by 99.52%. The difference between predicted (36.05%) and objectively measured obesity prevalence (36.03%) was statistically nonsignificant. The models may be used to reliably estimate obesity prevalence in US adults using data from population health surveys.

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“Democratizing” artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

Cited by 20 publications

References 58 publications

The role of FAIR nanosafety data and nanoinformatics in achieving the UN sustainable development goals: the NanoCommons experience

The role of FAIR nanosafety data and nanoinformatics in achieving the UN sustainable development goals: the NanoCommons experience

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Building Machine Learning Models to Correct Self-Reported Anthropometric Measures

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