Unintended consequences of machine learning in medicine?

By L. McDonald, S. Ramagopalan, A. Cox, M. Oğuz. 2017

534 citations recorded by the source corpus.

Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not specific to ML, and thus the article may lead to an adverse perception about this technique in particular. Whilst ML is not without its limitations like any methodology, a balanced view is needed in order to not hamper its use in potentially enabling better patient care.

Subfields: Artificial Intelligence, Family Practice, Health Informatics

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Citations in the corpus

Scalable and accurate deep learning with electronic health records (2018, background): “Explanation of predictions A common criticism of neural networks is that they offer little insight into the factors that influence the prediction [9].”
Ensuring Fairness in Machine Learning to Advance Health Equity (2018, background): “Complex data and models can drive more personalized and accurate predictions but may also make algorithms hard to understand and trust (25).”
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence (2021, background): “…algorithms have begun to greatly impact human lives by guiding several consequential decision-making procedures such as hiring employees, assigning loans and credit scores, making medical diagnoses, and dealing with recidivism in the criminal justice system (see, for example, [1, 2, 3, 4, 5, 6]).”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “Unintended consequences of ML (52) Safe, effective, efficient and humanistic care.”
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning (2018, methodology): “In order to resolve the tension between accuracy and interpretability, which is crucial for clinical decision-making (Cabitza et al., 2017), the system presents the clinicians with a rule-based approximation for the learned ML pipeline by mining for logical associations between patients’ features an”
Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology (2018, background): “More research is required before we will fully understand the good, the bad, and the unintended consequences of ML in HE [47].”