2021 Jul; 4: 123144. Google Scholar WebMachine Learning and Ethics When new technology is introduced into healthcare, novel ethical dilemmas arise in the human-machine interface. RCTs are often run specifically to gather unbiased evidence of treatment effects. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. This is relevant to algorithmic problems focused on prediction, not just causal inference. Menstruation in adolescents: What do we know? Machine Learning in Health Care Data-augmentation and sampling methods may also be used to mitigate effects of model confounding. Draper 2016, Healthcare Robots: Ethics, Design and Implementation, London: Routledge. Choosing these patients can rely on parts of the EHR that may be skewed due to lack of access to care or due to abnormalities in clinical care: For example, economic incentives may alter diagnosis code logging (81), clinical protocol affects the frequency and observation of abnormal tests (62), historical racial mistrust may delay care and affect patient outcomes (82), and naive data collection can yield inconsistent labels in chest X-rays (56). However, because socioeconomic factors affect both access to healthcare and access to financial resources, these models may yield predictions that exacerbate inequities. Features selected by lasso may be colinear with other features not selected (110). 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). Individual fairness imposes classifier performance requirements that operate over pairs of individuals; e.g., similar individuals should be treated similarly (115). Recent work has shown that models trained with a surrogate loss may exhibit approximation errors that disproportionately affect undersampled groups in the training data (95). In clinical settings, researchers often select patient disease occurrence as the prediction label for models. We then examine how data collection processes in funded research can amplify inequity and unfairness. Here, we motivate the ethical considerations in the pipeline with a case study of Black mothers in the United States, who die in childbirth at a rate three times higher than white women (22). There are many factors that influence the selection of a research problem, In cases where health need is of highest importance, a natural solution is to choose another outcome definition besides healthcare costs, e.g., the number of chronic diseases as a measure of health needs. Davis SE, Lasko TA, Chen G, Siew ED, Matheny ME. As has been raised in previous sections, a crucial concern with model deployment is generalization. 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 Major points of the ethical discussion on ML applications in health care are closely linked to fundamental epistemic issues, such as inconclusive, inscrutable or The role of gender in scholarly authorship, Demographics and discussion influence views on algorithmic fairness. Development of a nomogram for prediction of vaginal birth after cesarean delivery, Stepwise regression and stepwise discriminant analysis need not apply here: a guidelines editorial, Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. 2020. First, data on group membership can be completely absent. This inequity is unjust because it connects to a history of reproductive injustices faced by Black women in the United States, from gynecological experimentation on enslaved women to forced sterilizations (23, 24). 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. The ethics of machine learning refers specifically to the questions of morality surrounding the outputs of machine learning models that use data, which come with their ethical concerns. Ethics CheXclusion: fairness gaps in deep chest X-ray classifiers. Creanga AA, Berg CJ, Ko JY, Farr SL, Tong VT, et al. There are many factors that influence the selection of a research problem, from interest to available funding. Even the most casually When data are collected, they are not digitized and often contain errors. 2002. Utilizing optical character recognition (OCR) technology on physicians' handwriting is one way that machine learning is used in healthcare. Automation in feature selection does not eliminate the need for contextual understanding. 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. Ethical guidelines can be created to catch up with the age of machine learning and artificial intelligence that is already upon us. 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. Overvaluing individual consent ignores risks to tribal participants, Disparities in foundation and federal support and development of new therapeutics for sickle cell disease and cystic fibrosis, NCAA genetic screening rule sparks discrimination concerns, Uncertain Suffering: Racial Health Care Disparities and Sickle Cell Disease. 2015. How can evaluation and audits of ML systems be translated into meaningful clinical practice when, in many countries, clinicians themselves are subject to only limited external evaluations or audits? Larger genomics datasets often target European populations, producing genetic risk scores that are more accurate in individuals of European ancestry than other ancestries (75). For example, the US Centers for Disease Control and Prevention suppresses numbers for counties with fewer than 10 deaths for a given disease (80). Although these data omissions occur because of patient privacy, such censoring on the dependent variable introduces particularly pernicious statistical bias, and as a result, much remains to be understood about what community, health facility, patient, and provider-level factors drive high mortality rates. The ancient fantasy of an intelligent machine or a smart homunculus became a research project in the 17 th century when Gottfried Leibniz, the philosopher and logician who, with Newton, discovered the infinitesimal calculus, suggested that human reason could be rendered in a universal Others may explicitly want to understand patients who will have high healthcare costs to reduce the total cost of healthcare (90). Similarly, one might choose to optimize the worst-case error across groups as opposed to the average overall error. Such frameworks should explicitly account for health disparities across the stages of ML development in health and ensure health equity audits as part of postmarket evaluation (143). Strategies for handling missing data in electronic health record derived data, Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are Parking and Directions. Some of this work, termed critical data studies, is from a social science perspective (9, 10), whereas other work leads with a technical and computer science perspective (1113). Learn how to find state- and county-level reports in the CDCs death and injury data. In each example, it is essential that model developers choose a reliable proxy and account for noise in the outcome labels, as these choices can have a large impact on performance and equity of the resulting model. Unjust differences in quality and outcomes of healthcare between groups often reflect existing societal disparities for disadvantaged groups. Federal government websites often end in .gov or .mil. They produce more novel research, but their innovations are taken up at lower rates (47). Overall, this evidence suggests that diversifying the scientific workforce will lead to problem selection that more equitably represents the interests and needs of the population as a whole. 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. These methods allow researchers to: Analyze complex, underused, high-burden, healthcare data WebArtificial intelligence (AI), which includes the fields of machine learning, natural language processing, and robotics, can be applied to almost any field in medicine, 2 and its potential Why is my classifier discriminatory? Cumulative quantitative assessment of blood loss. In, Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, A snapshot of the frontiers of fairness in machine learning, Calders T, Karim A, Kamiran F, Ali W, Zhang X. AI Ethics In The Age Of ChatGPT - What Businesses Need To Know The clinical interventions accompanying the clinical ML model should be analyzed to contextualize the use of the model in the clinical setting (147). Instead, we focus on equity in ML models that operate on health data. Recent work has focused on identifying and mitigating violations of fairness definitions in healthcare settings. 2013. Health Further, screening for sickle cell disease is viewed by some as unfair targeting (38), and Black patients with the disease who seek treatment are often maligned as drug abusers (39). 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 limitations in data acquisition. 2014. 8600 Rockville Pike Noseworthy PA, Attia ZI, Brewer LC, Hayes SN, Yao X, et al. WebBig data and machine learning algorithms for health-care delivery Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. Eliminating LGBTIQQ health disparities: the associated roles of electronic health records and institutional culture. Predicting patient cost blooms in Denmark: a longitudinal population-based study, Measuring racial/ethnic disparities in health care: methods and practical issues. Thi National Institutes Of Health (NIH) Advances Its Artificial Intelligence / Machine Learning Consortium To Advance Health Equity And Researcher Diversity (AIM-AHEAD) Jul 6, 2023, 12:44pm EDT 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. In this review, we have described the ethical considerations at each step of the ML model development pipeline we introduced. Creanga AA, Bateman BT, Mhyre JM, Kuklina E, Shilkrut A, Callaghan WM. WebBackground: Machine learning (ML) models can be used to predict future frailty in the community setting. As machine learning (ML) models proliferate into many aspects of our lives, there is growing concern regarding their ability to inflict harm. Nearly 60% of the burden of poverty-related neglected diseases occurs in western and eastern sub-Saharan Africa, as well as South Asia. We highlight a few in this illustration. These resulting inequities can lead to unintended and permanent embedding of biases in algorithms used for clinical care. For example, a higher fraction of NIH proposals from Black scientists study community and population-level health (50). ethics For example, 75% of Black women give birth at hospitals that serve predominantly Black patients (, Outcome definition: Once data are collected, the choice of outcome definition can obscure underlying issues, e.g., differences in clinical practice. MACHINE LEARNING IN HEALTHCARE Proposals from white researchers in the United States are more likely to be funded by the NIH than proposals from Black researchers (50, 51), which in turns affects what topics are given preference. Maternal mortality and morbidity in the United States: Where are we now? Sagawa S, Raghunathan A, Koh PW, Liang P. 2020. In addition to RCTs and EHRs, healthcare billing claims data, clinical registries, and linked health survey data are also common data sources in population health and health policy research (67, 68), with known biases concerning which populations are followed and who is able to participate.
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