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Machine Learning and Health Care: Potential Benefits and Issues.

J Graham Atkinson1, Elizabeth G Atkinson

  • 1District of Columbia (Dr J. G. Atkinson); and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas (Dr E. G. Atkinson).

The Journal of Ambulatory Care Management
|January 17, 2023
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Summary
This summary is machine-generated.

Machine learning (ML) and artificial intelligence (AI) can advance healthcare, but require careful implementation to prevent bias. Addressing data disparities, especially in genetics, is crucial for equitable AI in medicine.

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Area of Science:

  • Healthcare technology
  • Biomedical informatics
  • Artificial intelligence in medicine

Background:

  • Machine learning (ML) and artificial intelligence (AI) offer transformative potential for healthcare delivery and outcomes.
  • Ensuring equitable and unbiased implementation of these technologies is paramount to avoid exacerbating existing health disparities.

Purpose of the Study:

  • To explore the potential benefits of ML and AI in healthcare.
  • To highlight critical considerations and potential pitfalls, particularly concerning data bias and equitable access.
  • To emphasize the need for addressing data composition issues in large-scale datasets.

Main Methods:

  • Review of current literature on ML and AI applications in healthcare.
  • Analysis of data bias issues, with a specific focus on genetics databases.
  • Discussion of strategies for mitigating bias in algorithm training and interpretation.

Main Results:

  • ML and AI can significantly improve healthcare diagnostics, treatment, and operational efficiency.
  • Significant biases exist in current datasets, notably the overrepresentation of individuals of European descent in genetics databases.
  • Unaddressed data disparities can lead to biased algorithm performance and inequitable health outcomes.

Conclusions:

  • Careful consideration of data sources and potential biases is essential for the responsible development and deployment of AI in healthcare.
  • Proactive measures are needed to ensure ML algorithms do not perpetuate or amplify health inequities.
  • Future research should focus on diversifying datasets and developing bias-detection/mitigation techniques for equitable AI.