Aims: To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners.
Methods: We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants' responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA.
Results: We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of "broad consent", respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI's abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare.
Conclusion: Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology.
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http://dx.doi.org/10.1186/s12910-024-01008-0 | DOI Listing |
Eur J Med Res
January 2025
Clinical Research and Big Data Center, South China Research Center for Acupuncture and Moxibustion, Medical College of Acu-Moxi and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China.
Objectives: Poststroke dysphagia (PSD) is a common complication after stroke but there is limited information on its global prevalence and influencing factors, such as spatial, temporal, demographic characteristics, and stroke-related factors. Our study seeks to fill this knowledge gap by exploring the overall prevalence of PSD and its influencing factors.
Methods: A search of English-language literature from database inception from 2005 until May 2022 was performed using PubMed, Embase, Web of Science, Cochrane Library, and Scopus.
Eur J Med Res
January 2025
Medical Big Data Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, 28 Fuxing RD., Beijing, 100853, China.
Background: Chronic kidney disease (CKD) carries the highest population attributable risk for mortality among all comorbidities in chronic heart failure (CHF). No studies about the association between inferior vena cava (IVC) diameter and all-cause mortality in patients with the comorbidity of CKD and CHF has been published.
Methods: In this retrospective cohort study, a total of 1327 patients with CHF and CKD were included.
Behav Res Methods
January 2025
Department of Psychology, University of Notre Dame, 390 Corbett Hall, Notre Dame, IN, 46556, USA.
Aberrant responses (e.g., careless responses, miskeyed items, etc.
View Article and Find Full Text PDFSci Rep
January 2025
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.
Given the increasing urban population and frenetic mobility, understanding how individuals perceive crowding at large-scale events is crucial for effective crowd management and safety. This study focuses on Tokyo Big Sight in Japan exhibitions to examine participants' perceptions of peak crowding times, locations, and local density, and compare them with the actual measurements. Our methodology integrated questionnaires with beacon tag data.
View Article and Find Full Text PDFMed Intensiva (Engl Ed)
January 2025
Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam Cardiovascular Science, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, University of Amsterdam, Vrije Universiteit, Amsterdam, The Netherlands.
Objective: To determine whether the ROX index and its variations can predict the risk of intubation in ICU patients receiving NIV ventilation using large public ICU databases.
Design: Retrospective observational cohort study.
Setting: Patient data was extracted from both the AmsterdamUMCdb and the MIMIC-IV ICU databases, which contained data related to 20,109 and 50,920 unique patients.
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