# From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence arXiv: https://arxiv.org/abs/2308.02448 ยท HTML: https://mlsanity.com/paper/2308.02448/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 105 references that are in the map. - First posted on arXiv 4 Aug 2023. ## Builds on (most cited first, 30 of 32) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022) - [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023) - [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022) - [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://mlsanity.com/paper/2305.13860/) (2023) - [Understanding Diffusion Models: A Unified Perspective](https://mlsanity.com/paper/2208.11970/) (2022) - [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023) - [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022) - [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022) - [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021) - [A Study of Generative Large Language Model for Medical Research and Healthcare](https://mlsanity.com/paper/2305.13523/) (2023) - [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023) - [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023) - [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021) - [The Self-Perception and Political Biases of ChatGPT](https://mlsanity.com/paper/2304.07333/) (2023) - [ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://mlsanity.com/paper/2303.16421/) (2023) - [Metrics for Deep Generative Models](https://mlsanity.com/paper/1711.01204/) (2017) - [The Forgotten Margins of AI Ethics](https://mlsanity.com/paper/2205.04221/) (2022) - [Generating Fake Cyber Threat Intelligence Using Transformer-Based Models](https://mlsanity.com/paper/2102.04351/) (2021) - [Differentiate ChatGPT-generated and Human-written Medical Texts](https://mlsanity.com/paper/2304.11567/) (2023) - [Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization](https://mlsanity.com/paper/2305.15008/) (2023) - [Towards Explainable Evaluation Metrics for Natural Language Generation](https://mlsanity.com/paper/2203.11131/) (2022) - [Understanding Emails and Drafting Responses -- An Approach Using GPT-3](https://mlsanity.com/paper/2102.03062/) (2021) ## Built on by (most cited first, 3 of 3) - [Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work](https://mlsanity.com/paper/2312.10057/) (2023) - [Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities](https://mlsanity.com/paper/2312.14647/) (2023) - [Governance of Generative Artificial Intelligence for Companies](https://mlsanity.com/paper/2403.08802/) (2024) ## Abstract (arXiv) 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 captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about 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, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare.