Can medical algorithms be fair? Three ethical quandaries and one dilemma

Bærøe, Kristine; Gundersen, Torbjørn; Henden, Edmund; Rommetveit, Kjetil

doi:10.1136/bmjhci-2021-100445

Cited by 12 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a central dilemma when applying AI in medical treatment: who is ethically accountable for a decision which arises from the cooperation of a physician and an AI tool 73 ? Due to the ‘black box’ character of the most AI algorithms, it is difficult for physicians to understand how these algorithms create their recommendations.…”

Section: Discussionmentioning

confidence: 99%

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

et al. 2022

View full text Add to dashboard Cite

Objective The attitudes about the usage of artificial intelligence in healthcare are controversial. Unlike the perception of healthcare professionals, the attitudes of patients and their companions have been of less interest so far. In this study, we aimed to investigate the perception of artificial intelligence in healthcare among this highly relevant group along with the influence of digital affinity and sociodemographic factors. Methods We conducted a cross-sectional study using a paper-based questionnaire with patients and their companions at a German tertiary referral hospital from December 2019 to February 2020. The questionnaire consisted of three sections examining (a) the respondents’ technical affinity, (b) their perception of different aspects of artificial intelligence in healthcare and (c) sociodemographic characteristics. Results From a total of 452 participants, more than 90% already read or heard about artificial intelligence, but only 24% reported good or expert knowledge. Asked on their general perception, 53.18% of the respondents rated the use of artificial intelligence in medicine as positive or very positive, but only 4.77% negative or very negative. The respondents denied concerns about artificial intelligence, but strongly agreed that artificial intelligence must be controlled by a physician. Older patients, women, persons with lower education and technical affinity were more cautious on the healthcare-related artificial intelligence usage. Conclusions German patients and their companions are open towards the usage of artificial intelligence in healthcare. Although showing only a mediocre knowledge about artificial intelligence, a majority rated artificial intelligence in healthcare as positive. Particularly, patients insist that a physician supervises the artificial intelligence and keeps ultimate responsibility for diagnosis and therapy.

show abstract

Section: Discussionmentioning

confidence: 99%

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Other work has emphasised the need for operationalizing equity and fairness in AI for healthcare. 11 , 12 Recent work addressing this call has discussed ethical considerations of fairness and equity in the context of AI for healthcare, 42 , 43 , 44 , 45 , 46 , 47 suggested best practices to incorporate health equity in the algorithm development lifecycle, 14 , 26 , 27 , 28 , 29 , 48 and proposed operational definitions. 30 , 31 , 32 Existing operational definitions have largely borrowed from the AI fairness literature, 21 , 22 , 23 , 24 proposing metrics based on statistical parity in AI performance across subpopulations.…”

Section: Discussionmentioning

confidence: 99%

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Schaekermann,

Spitz,

Pyles

et al. 2024

eClinicalMedicine

View full text Add to dashboard Cite

“…Various biases can creep into algorithmic development and application, affecting the fairness of such algorithms. 9 A range of protected attributes, factors that should not influence health, have been chosen because of legal mandates or because of organizational values. 65 Some common protected attributes include race, ethnicity, religion, national origin, gender, marital status, age, and socioeconomic status.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Individual Fairness and Healthcare: A Scoping Review

Anderson,

Visweswaran

2024

Preprint

View full text Add to dashboard Cite

Statistical and artificial intelligence algorithms are increasingly being developed for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorithmic fairness. Individual fairness in algorithms constrains algorithms to the notion that "similar individuals should be treated similarly." We conducted a scoping review on algorithmic individual fairness to understand the current state of research in the metrics and methods developed to achieve individual fairness and its applications in healthcare. We searched three databases, PubMed, ACM Digital Library, and IEEE Xplore, for algorithmic individual fairness metrics, algorithmic bias mitigation, and healthcare applications. Our search was restricted to articles published between January 2013 and September 2023. We identified 1,886 articles through database searches and manually identified one article from which we included 30 articles in the review. Data from the selected articles were extracted, and the findings were synthesized. Based on the 30 articles in the review, we identified several themes, including philosophical underpinnings of fairness, individual fairness metrics, mitigation methods for achieving individual fairness, implications of achieving individual fairness on group fairness and vice versa, fairness metrics that combined individual fairness and group fairness, software for measuring and optimizing individual fairness, and applications of individual fairness in healthcare. While there has been significant work on algorithmic individual fairness in recent years, the definition, use, and study of individual fairness remain in their infancy, especially in healthcare. Future research is needed to apply and evaluate individual fairness in healthcare comprehensively.

show abstract

Can medical algorithms be fair? Three ethical quandaries and one dilemma

Cited by 12 publications

References 22 publications

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Algorithmic Individual Fairness and Healthcare: A Scoping Review

Contact Info

Product

Resources

About