Machine learning has the potential to revolutionize healthcare by improving diagnosis, treatment, and patient outcomes. However, the implementation of machine learning in healthcare also raises a number of ethical challenges that must be addressed to ensure that these technologies are used responsibly and for the benefit of all. In this article, we will explore these ethical challenges and discuss potential frameworks and guidelines for addressing them.
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Electronic health record data is a valuable resource for machine learning in healthcare. By analyzing large amounts of patient data, machine learning algorithms can identify patterns and make predictions that can improve healthcare quality, efficiency, and accessibility. However, there are also concerns about the potential for biases in machine learning algorithms that use electronic health record data.
One potential source of bias is missing or incomplete data. Disadvantaged populations are more likely to receive care in multiple health systems. This can result in relevant data being missing from an individual health system’s records (Gianfrancesco et al., 2018). This can hinder the performance of machine learning algorithms and lead to inaccurate predictions or decisions.
Another potential source of bias is implicit bias in the care that disadvantaged populations receive. If the care provided to these populations is biased, this bias may be reflected in the data used to train machine learning algorithms. As a result, the algorithms may replicate and amplify existing disparities (Char et al., 2018).
To address these concerns, it is important to ensure that electronic health record data is complete, accurate, and representative. This may involve improving data collection and sharing practices, as well as addressing implicit biases in healthcare delivery. It is also important to ensure that machine learning algorithms are transparent and accountable so that any potential biases can be identified and addressed.
A Belmont Report for Health Data
The Belmont Report established ethical principles and guidelines for research involving human subjects. These principles include respect for persons, beneficence, and justice. However, the collection, use, and sharing of health data pose unique challenges that require a new ethical framework.
One of the main challenges is privacy. Health data is sensitive and personal. Its collection and use must respect individuals’ rights to privacy and confidentiality (Parasidis et al., 2019). Another challenge is consent. Individuals must be informed about how their health data will be used. They must have the opportunity to provide or withhold consent.
A new ethical framework for health data must address these challenges by establishing clear principles and guidelines for the collection, use, and sharing of health data. This framework should prioritize individuals’ rights to privacy and consent while also balancing societal interests in improving healthcare quality and public health.
Public Preferences About Secondary Use of Electronic Health Information
The secondary use of electronic health information refers to the use of this information for purposes other than direct patient care. This can include research, quality improvement, public health surveillance, and other activities that aim to improve healthcare delivery.
Public preferences about the secondary use of electronic health information vary depending on the purpose and context of use. While there is general support for using electronic health information for improving healthcare quality and public health (Grande et al., 2013), there is less support for using this information for marketing or commercial purposes.
These preferences highlight the importance of ensuring that the secondary use of electronic health information respects individuals’ rights to privacy and consent. It is important to provide individuals with clear information about how their health data will be used. We must obtain their informed consent before using their data for secondary purposes.
Frameworks and Principles for Ethical AI in Healthcare
Several frameworks and principles have been proposed to promote ethical AI in healthcare. These frameworks aim to ensure that AI systems are transparent, accountable, fair, and respectful of human rights.
One such framework is the Rome Call for AI Ethics. This document promotes an ethical approach to AI that respects human dignity and rights. It fosters education and learning while protecting the rights and interests of individuals and communities. Another framework is the IEEE’s Ethically Aligned Design document. This document provides five principles for creating ethical AI systems that prioritize human rights, well-being, transparency, and accountability.
These frameworks provide valuable guidance for addressing the ethical challenges of implementing machine learning in healthcare. By following these principles and guidelines, we can ensure that AI systems are used responsibly and for the benefit of all.
Machine learning has the potential to greatly improve healthcare delivery by providing more accurate diagnoses, more effective treatments, and better patient outcomes. However, it is important to address the ethical challenges that arise from its implementation.
By developing new ethical frameworks; guidelines; ensuring transparency; accountability; carefully considering; implementation; we can ensure that these technologies are used responsibly; benefit all.
I invite you to join us on our journey of discovery; learning as we explore these complex issues; welcome your feedback; dialogue through Twitter or by commenting on this article.
Gianfrancesco Milena A., Tamang Suzanne, Yazdany Jinoos, Schmajuk Gabriela. (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine, 178, 1544–47.