Life and death decisions have always been part of the medical profession. Modem discussions on resource allocation in health care deal with such critical situations, and seek ethical solutions that will benefit individuals and society as well as conserve scarce resources. Deontological ethics and utilitarianism are opposing ethical views, each with its own theory on solving moral dilemmas. Utilitarian logic aims at maximizing the benefit for the greatest number of people, while deontological theories strive to uphold pervasive moral principles. Jewish thought has always confronted the toughest of human predicaments head-on. As we review part of the Jewish discourse on distributive justice throughout the ages, we will show its relevance to modem discussions on medical resource allocation. As in modem secular ethics, Jewish thought juxtaposes the two aforementioned philosophical viewpoints, and constantly attempts to reconcile them. Extracting from each theory its strengths, the ethical conclusions reached in Jewish religious texts are relevant to issues of resource allocation throughout the ages.
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Digit Health
January 2025
Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
The application of artificial intelligence (AI) to healthcare in Africa has the potential to transform productivity, diagnosis, disease surveillance, and resource allocation by improving accuracy and efficiency. However, to fully realize its benefits, it is necessary to consider issues concerning data privacy, equity, infrastructure integration, and ethical policy development. The use of these tools may improve the detection of diseases, the distribution of resources, and the continuity of care.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Radiation Oncology, Weifang People's Hospital, Weifang, China.
Background: Conditional survival (CS) analysis can estimate further survival probabilities based on the time already survived, providing dynamic updates for prognostic information. This study aimed to develop a CS-nomogram to promote individualized disease management for stage III non-small cell lung cancer (NSCLC).
Methods: This study included patients diagnosed with stage III NSCLC in the Surveillance, Epidemiology, and End Results database from 2010 to 2017 ( = 3,512).
Front Sports Act Living
January 2025
School of Management, Canadian University Dubai, Dubai, United Arab Emirates.
Context: This study was inspired by the considerable risks and diminishing enthusiasm among societies to invest in Olympic agendas, which traditionally involve billions of dollars, various opportunities, and complexities for host countries.
Purpose: The objective of this study was to evaluate the risks and benefits of long-term equity investments for companies and governments engaged in the Olympic movement.
Method: Qualitative methodologies were employed for this research, utilizing a multi-case approach that included 38 comprehensive interviews with companies and entities impacted by the Rio 2016 and Tokyo 2020 Olympic Games.
Front Public Health
January 2025
Sanitation Teaching and Research Section, Department of Health Service, Naval Medical University, Shanghai, China.
Background: Norovirus remains a significant viral cause of waterborne and foodborne gastroenteritis outbreaks and epidemics worldwide. The burden of norovirus extends across different income settings.
Methods: Leveraging secondary data from the 2021 Global Burden of Diseases Study, our analysis spanned the period from 1990 to 2021 to assess the burden of norovirus-associated diseases (NADs).
Front Public Health
January 2025
School of Economics, Liaoning University, Shenyang, China.
Urban public health resilience has become a critical focus in the transition to high-quality development, especially in addressing increasing public health challenges. This study explores the role of artificial intelligence (AI) technology in enhancing urban public health resilience across 284 Chinese cities from 2011 to 2021. Using a comprehensive index based on resistance, recovery, and innovation dimensions, the study quantifies AI technology levels through patent applications and authorizations, further disaggregating these into invention, utility model, and design patents.
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