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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.

Experimentally measured values of the laminar flame speed (SL) are reported for the primary reference fuels over a range of unburned-gas temperatures (Tu) spanning from room temperature to above 1,000 K, providing the highest-temperature SL measurements ever reported for gasoline-relevant fuels. Measurements were performed using expanding flames ignited within a shock tube and recorded using side-wall schlieren imaging. The recently introduced area-averaged linear curvature (AA-LC) model is used to extrapolate stretch-free flame speeds from the aspherical flames. High-temperature SL measurements are compared to values simulated using different kinetic mechanisms and are used to assess three functional forms of empirical SL–Tu relationships: the ubiquitous power-law model, an exponential relation, and a non-Arrhenius form. This work demonstrates the significantly enhanced capability of the shock-tube flame speed method to provide engine-relevant SL measurements with the potential to meaningfully improve accuracy and reduce uncertainty of kinetic mechanisms when used to predict global combustion behaviors most relevant to practical engine applications.