A governance model for the application of AI in health care
By Sandeep Reddy, Sonia Allan, Simon Coghlan, Paul Cooper. J. Am. Medical Informatics Assoc., 2019
690 citations recorded by the source corpus.
As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care.
Subfields: Health Informatics, Pharmacy, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/8d272a9948a8b97c3798f233d3631d466688da1b
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Citations in the corpus
- Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, methodology): “▪ Model interpretability : Generative AI models can be complex and difficult to interpret, making it challenging for clinicians to understand how the model arrived at its conclusions [13, 67].”
- Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, background): “Regulatory authorities are yet to formalize standards to evaluate and maintain the safety and impact of AI in many countries [19].”
- Ethics and governance of trustworthy medical artificial intelligence (2023, background): “It enables AI systems to generate universal ethical principles from specific ethical situations through machine learning and self-organization [78].”
- Bridging the Gap Between Ethics and Practice (2020, background): “Implementations of safety cultures in HCAI-based organizations is emerging with initial efforts to support AI governance in medical care [19, 93].”
- Ethical principles for artificial intelligence in education (2022, background): “…regarding ethics of data exploitation in decision making and interventions occur cross-disciplines (Jalal et al., 2021 ; Farris, 2021 ; medical care as in Reddy et al., 2020 ) or human resources management, as in Tambe et al., (2019); sports per - formance analysis as in Araújo et al., ( 2021 ).”
- A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT (2023, background): “On the contrary, AIGC for high-stakes applications, including healthcare [248], financial services [249], autonomous vehicles [250], and science discovery [251], are still challenging.”
- Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives (2024, background): “An example of this can be seen with AI devices gathering and transmitting data from older adults in their homes without their knowledge; these services may provide the collected data to AI developers without patients’ consent [124].”