Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and "100 years from now." (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635466 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0294028 | PLOS |
Crit Care Explor
January 2025
Division of Pediatric Critical Care Medicine, Department of Pediatrics, Indiana University School of Medicine/Riley Children's Health, Indianapolis, IN.
Objectives: To investigate the prevalence of pulmonary embolism (PE) in children admitted to critical care diagnosed with COVID-19 infection.
Design: Retrospective database study.
Setting: Data reported to the Virtual Pediatric Systems, 2018-2021.
PLoS One
January 2025
Interventional Psychiatry Program, St. Michael's Hospital, Toronto, Ontario, Canada.
Background: Posttraumatic stress disorder (PTSD) affects 3.9% of the general population. While massed cognitive processing therapy (CPT) has demonstrated efficacy in treating chronic PTSD, a substantial proportion of patients still continue to meet PTSD criteria after treatment, highlighting the need for novel therapeutic approaches.
View Article and Find Full Text PDFPLOS Glob Public Health
January 2025
Institute of Anatomy, Faculty of Medicine, University of Zurich, Zurich, Switzerland.
Peru is among Latin American countries with the largest Indigenous population, yet ethnical health disparities persist, particularly in the Amazon region which comprises 60% of the national territory. Healthcare models that include Indigenous medicine and traditional healers present an important avenue for addressing such inequalities, as they increase cultural adequacy of services, healthcare access, and acknowledge Indigenous Rights for their perspectives to be represented in public healthcare. Understanding the underlying epistemologies of Indigenous medicine is a prerequisite for this purpose.
View Article and Find Full Text PDFNicotine Tob Res
January 2025
Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.
Introduction: The U.S. Food and Drug Administration's (FDA) pursuit of a low nicotine standard for cigarettes raises concerns that a focus on cigarettes may encourage people to use other combusted tobacco products, undermining the policy's effectiveness.
View Article and Find Full Text PDFDiabetes Care
January 2025
Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ.
Objective: We derive and validate D-RISK, an electronic health record (EHR)-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (prediabetes + diabetes) in clinical practice.
Research Design And Methods: We used retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backward selection was used to predict dysglycemia (HbA1c ≥5.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!