ethics of machine learning in healthcare{ keyword }

Abstract: The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Do no harm: a roadmap for responsible machine learning for health care. While there is scholarship addressing social implications and algorithmic fairness in general, there has been less work at the intersection of health, ML, and fairness (1416), despite the potential life-or-death impacts of ML models (8, 17). WebIn this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. 2019. Because the analysis controls for health needs, the disparities are solely a result of differences in healthcare access and systemic discrimination (92). Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning Melissa D McCradden , James A Anderson , Elizabeth A. Stephenson , Erik Drysdale , Lauren Erdman , Anna Goldenberg & show all Pages 8-22 | Published online: 20 Jan 2022 Download citation https://doi.org/10.1080/15265161.2021.2013977 In this article Full In the United States, it was only in 2016, with the release of the US Transgender Survey, that there was a meaningfully sized dataset28,000 respondentsto enable significant analysis and quantification of discrimination and violence that transgender people face (77). Data collection should be framed as an important front-of-mind concern in the ML modeling pipeline, clear disclosures should be made about imbalanced datasets, and researchers should engage with domain experts to ensure that data reflecting the needs of underserved and understudied populations are gathered. A comprehensive evaluation of multi-task learning and multi-task pre-training on EHR time-series data, Oakden-Rayner L, Dunnmon J, Carneiro G, R C. 2020. The ethical issues of the application of artificial intelligence in As artificial intelligence (AI), machine learning (ML) and big data can exhaust human oversight and memory capacity, this will give rise to many of these new dilemmas.Technology has little When individuals from disadvantaged communities appear in observational datasets, they are less likely to be accurately captured due to errors in data collection and systemic discrimination. First, we look at problem selection, and explain how funding for ML for health research can lead to injustice. In, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, et al. Here, we outline Black-white differences in severe maternal morbidity and site of care. For this to happen, computer and data scientists and clinical entrepreneurs argue that one of the most critical aspects of healthcare reform will be artificial intelligence Simply controlling for confounding features by including them as features in classification or regression models may be insufficient to train reliable models because features can have a mediating or moderating effect (posttreatment effect on outcomes of interest) and have to be incorporated differently into model design (99). Fairness through awareness. Major points of the ethical discussion on ML applications in health care are closely linked to fundamental epistemic issues, such as inconclusive, inscrutable or HHS Vulnerability Disclosure, Help For instance, a positive label may be reliable, but the omission of a positive label could indicate either a negative label (i.e., no disease) or merely a missed positive label. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are Black mothers in the wealthiest neighborhoods in Brooklyn, New York have worse outcomes than white, Hispanic, and Asian mothers in the poorest ones, demonstrating a gap despite factors that should improve Black mothers outcomesliving in the same place and having a higher incomelikely due to societal bias that impacts Black women (, Postdeployment considerations: Finally, after a model is trained, postdeployment considerations may not fully consider the impact of deploying a biased prediction model into clinical settings that have large Black populations. Are current tort liability doctrines adequate for addressing injury caused by AI? score or other quantitative representation of a models quality and ability to achieve goals, a measure of the sensitivity and specificity of a model for each decision threshold, a measure of precision and recall of a model for each decision threshold, a measure of how well ML risk estimates reflect true risk. Double/debiased machine learning for treatment and structural parameters. However, these algorithms have significant limitations, as they typically require assumptions about the nature or amount of distributional shift an algorithm can accommodate. Because differences in label noise result in disparities in model impact, researchers have the responsibility to choose and improve disease labels so that these inequalities do not further exacerbate disparities in health. However, data noise and missingness can cause unjust inequities that impact populations in different ways. The general public became aware of the deep connection between big data analytics and public health during the COVID-19 pandemic. In, Proceedings of the Conference on Fairness, Accountability, and Transparency. Some, like that of Reference 136, may require a clear indication of which distributions in a healthcare pipeline are expected to change, and may develop models for prediction accordingly. Regulation of predictive analytics in medicine, Decolonial AI: decolonial theory as sociotechnical foresight in artificial intelligence, Lyndon A, McNulty J, VanderWal B, Gabel K, Huwe V, Main E. 2015. Zafar MB, Valera I, Rogriguez MG, Gummadi KP. WebThe present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Google is testing an artificial-intelligence program trained to expertly answer medical questions, racing against rivals The geometry of ROC space: understanding machine learning metrics through ROC isometrics. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. 1 Introduction. 2019. Caption If used carefully, this technology could improve performance in health care and potentially reduce inequities, says Assistant Professor Marzyeh Ghassemi. Domingos, Pedro, 2015, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, London: Allen Lane. A growing body of literature wrestles with the social implications of ML and technology. There are many factors that influence the selection of a research problem, from interest to 3. Ginther DK, Schaffer WT, Schnell J, Masimore B, Liu F, et al. the relation that determines the error between algorithm output and a given label, which the algorithm uses to optimize. Note that robust reporting of results should include an explicit statement of other nonoptimized metrics, including the original intended use case, the training cohort and case, or the level of model uncertainty. In the United States, however, cystic fibrosis receives 3.4 times more funding per affected individual from the US National Institutes of Health (NIH), the largest funder of US clinical research, and hundreds of times more private funding (37). Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. As machine learning ( ML) models proliferate into many aspects of our lives, there is growing concern 2. There is a growing methodological literature designing methods to generalize RCT treatment effects to other populations (60). Currently, when egregious cases of injustice are discovered only after clinical impact has already occurred, what can developers do to engage? 2007. General model outcome definitions for maternal health complications might overlook conditions specific to Black mothers, e.g., fibroids (, Algorithm development: During algorithm development, models may not be able to account for the confounding presence of societal bias. Several algorithms have recently been proposed to account for distribution shifts in data (135, 136). Many of these assumptions may be verifiable. For example, the outcome label for developing cardiovascular disease could be defined through the occurrence of specific phrases in clinical notes. A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning Melissa D McCradden , James A Anderson , Elizabeth A. Stephenson , Erik Drysdale , Lauren Erdman , Anna Goldenberg & show all Pages 8-22 | Published online: 20 Jan 2022 Download citation https://doi.org/10.1080/15265161.2021.2013977 In this article Full WebThi Along with potential benefits to healthcare delivery, machine learning healthcare applications (ML-HCAs) raise a number of ethical concerns. Bartlett VL, Dhruva SS, Shah ND, Ryan P, Ross JS. government site. AI and Ethics Article Original Research Open Access Published: 12 September 2021 Moral exemplars for the virtuous machine: the clinicians role in ethical artificial intelligence for healthcare Sumeet Hindocha & Cosmin Badea AI and Ethics 2 , 167175 ( 2022) Cite this article 4078 Accesses 9 Citations 7 Altmetric Metrics Abstract According to a 2023 survey by the Canadian software firm BlackBerry Limited, 82% of IT decision-makers in North America, the United Kingdom and Australia plan to invest in AI-driven cybersecurity by 2025. Specifically, we frame ethics of ML in healthcare through the lens of social justice. errors or otherwise obscuring information that affects the quality of the labels. Here, we The .gov means its official. Machine Learning Models to Predict Future Frailty in Community Apart from health documentation concerns, transgender people are often concerned about their basic physical safety when reporting their identities. In, Proceedings of the ACM Conference on Health, Inference, and Learning. MIT scientists build a system that can generate AI models for ChatGPT - An Ethical Nightmare Or Just Another Technology? In, 2013 IEEE 13th International Conference on Data Mining. Failing loudly: an empirical study of methods for detecting dataset shift. Structural Disparities in Data Science: A Prolegomenon for the Future of Machine Learning AI Ethics Is Not a Panacea An Ethical Framework to Nowhere Respect and Trustworthiness in the Patient-Provider-Machine Relationship: Applying a Relational Lens to Machine Learning Healthcare Applications An Evaluation of the Pipeline Framework for For example, sickle cell disease and cystic fibrosis are both genetic disorders of similar severity, but sickle cell disease is more common in Black patients, while cystic fibrosis is more common in white patients. Speci cally, we frame ethics of ML in health care through the lens of social justice. How these principles could inform the ML model development ethical pipeline remains understudied. In ML, penalized regressions like lasso regression are popular for automated feature selection, but the lasso trades potential increases in estimation bias for reductions in variance by shrinking some feature coefficients to zero. Human ethical duties and responsibilities may become occluded or altered when ML is used. Additionally, there is the possibility that models may help to debias current clinical care by reducing known biases against minorities (127) and disadvantaged majorities (128). AI Ethics In The Age Of ChatGPT - What Businesses Need To Know [2009.10576] Ethical Machine Learning in Health Care Recruiting machine-learning researchers can be a time-consuming and financially costly process for science and engineering labs. Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Learn how to find state- and county-level reports in the CDCs death and injury data. Coll. Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. These survey data resonate to the ethical and regulatory challenges that surround AI in healthcare, particularly privacy, data fairness, accountability, transparency, and liability. Delayed care and mortality among women and men with myocardial infarction, A machine learning framework for plan payment risk adjustment, Upcoding: evidence from medicare on squishy risk adjustment, Natarajan N, Dhillon IS, Ravikumar PK, Tewari A. Boag W, Suresh H, Celi LA, Szolovits P, Ghassemi M. 2018. The complexity and size of data surpasses detailed oversight and accountability by humans. The strength of ML resides in its capacity to learn from data without need to be explicitly programmed (Samuel, 1959 ); ML algorithms are autonomous and self Health Bugiardini R, Ricci B, Cenko E, Vasiljevic Z, Kedev S, et al. If a model developer is most concerned with cost, it is possible to correct for health disparities in predicting healthcare costs by building fairness considerations directly into the predictive model objective function (93). If the outcome label has ethical bias, the source of inequity should be accounted for in ML model design, leveraging literature that attempts to remove ethical biases during preprocessing, or with use of a reasonable proxy. 2014. In the latter example, default settings in R for classification will allow trees to grow until there is just one observation in a terminal leaf. According to a 2023 survey by the Canadian software firm BlackBerry Limited, 82% of IT decision-makers in North America, the United Kingdom and Australia plan to invest in AI-driven cybersecurity by 2025. the process whereby data can be lost in collection due to the data type, meaningless information added to data that obscures the underlying information of the data, the manner in which data are absent from a sample of the population. For model developers seeking to optimize for health needs, healthcare costs can deviate from health needs on an individual level because of patient socioeconomic factors. Thus the treatment policy was a confounding factor in a seemingly straightforward prediction task by altering the data such that patients with asthma were erroneously predicted by models to have lower risk of dying from pneumonia. Clinical diagnosis is a fundamental task for clinical prediction models, e.g., models for computer-aided diagnosis from medical imaging. Algorithmic solutions to improve treatment are starting to transform health care. The authors thank Rediet Abebe for helpful discussions and contributions to an early draft and Peter Szolovits, Pang Wei Koh, Leah Pierson, Berk Ustun, and Tristan Naumann for useful comments and feedback. PMLR 106 , 123 (2019). and. Methods to address the positive-unlabeled setting use estimated noise rates (87) or hand-curated labels from clinicians that are strongly correlated with positive labels, known also as silver-standard labels (88). Machine Learning in Healthcare However, RCTs have notoriously aggressive exclusion (or inclusion) criteria (57), which create study cohorts that are not representative of general patient populations (58). Halpern Y, Horng S, Choi Y, Sontag D. 2016. MIT scientists build a system that can generate AI models for AI, Machine Learning, and Ethics in Health Care - PubMed Tamang S, Milstein A, Srensen HT, Pedersen L, Mackey L, et al. 2020. Clear documentation enables insight into the model development and data collection. The role of gender in scholarly authorship, Demographics and discussion influence views on algorithmic fairness. Assessing racial/ethnic disparities in treatment across episodes of mental health care, Combating anti-blackness in the AI community. For example, undocumented immigrants may fear deportation if they participate in healthcare systems. Herrera-Perez D, Haslam A, Crain T, Gill J, Livingston C, et al. As one salient example, a large study of laboratory tests to model three-year survival found that healthcare process features had a stronger predictive value than the patients physiological feaures (62). The report of the 2015 US transgender survey, Risk as social context: immigration policy and autism in California, Maternal mortality in the United States: updates on trends, causes, and solutions, The impact of data suppression on local mortality rates: the case of CDC WONDER, Overbilling versus downcodingthe battle between physicians and insurers. Safety concerns are important in data collection for undocumented migrants, where sociopolitical environments can lead to individuals feeling unsafe during reporting opportunities. Moreover, facilities have incentives to underreport (81) and overreport (85, 86) outcomes, yielding differences in model representations.

The Delta Reservations, Religious Of The Good Shepherd, Articles E