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Abstract In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Autonomy for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.
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Consentful-by-design: a perspective on safeguarding data ownership from generative AI leveraging lessons from the healthcare domain
Research on Artificial Intelligence (AI) is lined with moral considerations. In healthcare, a high-risk field, sub-fields have emerged to mitigate AI-specific ethical issues such as fairness and transparency. However, similar considerations remain unaddressed beyond healthcare, and as generative AI tools (‘GenAI’) reach lay audiences, this neglect yields ethical concerns. The present work focuses on learning from ethical considerations in healthcare to mitigate challenges ofGenAI. We structure our proposed mitigation strategies around three of the five established biomedical and AI ethics principles (autonomy, transparency, beneficence), highlighting the risks GenAI poses for intellectual property owners (scraping of copyrighted data, unpaid labor, plagiarism, fraud). We propose concrete ways to affirm these principles on GenAI, using biomedical AI examples and the emerging frameworks they have sparked in domains of data ownership, federated learning, and data provenance. This article comes at a pivotal time for AI, generalizing ethics-aware principles to GenAI to open new research avenues toward responsible AI.
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- PAR ID:
- 10673172
- Editor(s):
- Gill, Karamjit
- Publisher / Repository:
- AI & Society: Knowledge, Culture and Communication
- Date Published:
- Journal Name:
- AI & SOCIETY
- ISSN:
- 0951-5666
- Subject(s) / Keyword(s):
- Artificial intelligence Generative AI Intellectual property Data ownership Labor exploitation AI ethics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1007/s00146-025-02817-6
