ReviewNPJ digital medicine2023
Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- From Algorithm to Policy: A Bibliometric Analysis of Implementation Science and Governance Frameworks in AI Healthcare Research (2016-2026).Healthcare (Basel, Switzerland) · 2026Review
- Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis.Communications medicine · 2026Article
- Portraying Ethical Risks of Medical AI: Mixed Methods Study From Connotation Definition to a Survey on Physicians' Cognition.Journal of medical Internet research · 2026Article
- Clinical large language model centered on electronic medical records.NPJ digital medicine · 2026Article
- Governance for safe and responsible AI in healthcare organisations: a scoping review of frameworks.NPJ digital medicine · 2026Article
- Article
- Addressing Autonomy Risks in Generative Chatbots with the Socratic Method.Science and engineering ethics · 2025Article
- Diabetic retinal disease.Nature reviews. Disease primers · 2025Review
- Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.Critical care (London, England) · 2025Review
- Perceptions and experiences of generative artificial intelligence training to support research for Chinese nurses: A qualitative focus group study.International journal of nursing sciences · 2025Article
- Racial, ethnic, and sex bias in large language model opioid recommendations for pain management.Pain · 2025Article
- Responsible Design, Integration, and Use of Generative AI in Mental Health.JMIR mental health · 2025Article
- Review
- Ethics of AI in healthcare: a scoping review demonstrating applicability of a foundational framework.Frontiers in digital health · 2025Review
- Bridging Distances and Enhancing Care: A Comprehensive Review of Telemedicine in Surgery.Cureus · 2024Review
- Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist.The Lancet. Digital health · 2024Article
- The performance of OpenAI ChatGPT-4 and Google Gemini in virology multiple-choice questions: a comparative analysis of English and Arabic responses.BMC research notes · 2024Article
- Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model.PLOS digital health · 2024Article
- Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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 Eutonomy, 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.