Implementing Machine Learning in Health Care — Addressing Ethical Challenges

By Danton S. Char, Nigam H. Shah, D. Magnus. 2018

1,243 citations recorded by the source corpus.

Implementing Machine Learning in Health Care We need to consider the ethical challenges inherent in implementing machine learning in health care if its benefits are to be realized. Some of these ch...

Subfields: General Health Professions, Health Informatics, Public Health, Environmental and Occupational Health

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Citations in the corpus

A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare (2023, background): “Specific arrangements may therefore need to be developed to provide necessary protection for patients, organisations, and clinicians using data for AI systems [17,41].”
YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024, background): “As with any machine learning system, addressing biases in training data and ensuring ethical considerations are paramount [91].”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “Ethical challenges of implementing ML in healthcare (58)”
Metrics Reloaded: Recommendations for image analysis validation (2022, neutral): “8 Example – neglecting the small size of structures: single-pixel differences can hugely impact the metric scores, which is especially relevant given high inter-rater variability and the non-deterministic nature of AI algorithms Example – inappropriate aggregation scheme: hierarchical data structure”
Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “Policymakers and regulatory authorities play a crucial role in ensuring AI fairness through establishing comprehensive guidelines, standards, and regulations that govern the devel‑ opment and deployment of AI in healthcare [95, 113–115].”
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data (2018, background): “…scientists and bioinformaticians, together with practitioners, biostatisticians, and epidemiologists, should outline the “intent behind the design,” 9(p982) including choosing appropriate questions and settings for machine learning use, interpreting findings, and conducting follow-up studies.”
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis (2020, background): “This tendency of AI systems to learn biased models, which reproduce social stereotypes and underperform in minority groups, is especially dangerous in the context of healthcare (11, 12).”
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, background): “Trust between public and healthcare systems is essential for effective healthcare delivery [36].”