Artificial intelligence technology shows the advantages of improving efficiency, reducing costs, shortening time, reducing the number of staff on site and achieving precise operations, making impressive research progress in the fields of drug discovery and development, but there are few reports on application in energetic materials. This paper addresses the high safety risks in the current nitrification process of energetic materials, comprehensively analyses and summarizes the main safety risks and their control elements in the nitrification process, proposes possibilities and suggestions for using artificial intelligence technology to enhance the "essential safety" of the nitrification process in energetic materials, reviews the research progress of artificial intelligence in the field of drug synthesis, looks forward to the application prospects of artificial intelligence technology in the nitrification of energetic materials and provides support and guidance for the safe processing of nitrification in the propellants and explosives industry.
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http://dx.doi.org/10.3390/molecules28041900 | DOI Listing |
JMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFJMIR Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Division of Surgery & Interventional Science, Faculty of Medical Sciences, University College London, London, United Kingdom.
Background: The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development.
View Article and Find Full Text PDFInteract J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
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