Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways, and examine human-AI interaction effects to explore automation bias. Operating an AI reader as a second reader or as a high confidence filter improves current screening outcomes by 1.9-2.5% in sensitivity and up to 0.6% in specificity, achieving 4.6-10.9% reduction in assessments and 48-80.7% reduction in human reads. Automation bias degrades performance in multi-reader settings but improves it for single-readers. This study provides insight into feasible approaches for AI-integrated screening pathways and prospective studies necessary prior to clinical adoption.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364867 | PMC |
| http://dx.doi.org/10.1038/s41467-024-51725-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Toward Human-Centered Artificial Intelligence for Users' Digital Well-Being: Systematic Review, Synthesis, and Future Directions.
JMIR Hum Factors
September 2025
Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, United States.
Background: As information and communication technologies and artificial intelligence (AI) become deeply integrated into daily life, the focus on users' digital well-being has grown across academic and industrial fields. However, fragmented perspectives and approaches to digital well-being in AI-powered systems hinder a holistic understanding, leaving researchers and practitioners struggling to design truly human-centered AI systems.
Objective: This paper aims to address the fragmentation by synthesizing diverse perspectives and approaches to digital well-being through a systematic literature review.
The Future of the Gambling Industry is AI: Insights from Expert Interviews on Human-AI Collaboration, Regulation, and Ethics.
J Gambl Stud
September 2025
Department of Tourism, Hospitality, and Event Management, University of Florida, Gainesville, FL, 32611, USA.
In this study, a total of 41 experts were interviewed in two phases (2021 and 2023). The interviews were transcribed and examined with advanced machine learning models like k-mean clustering and BERT. The findings revealed five main themes: human-AI collaboration, regulatory changes, AI model development, gaming system and player engagement, and AI ethics and risks.
View Article and Find Full Text PDFArtificial intelligence in headache medicine: between automation and the doctor-patient relationship. A systematic review.
J Headache Pain
September 2025
Department of Neurology, Clínica Universidad de Navarra, Pamplona, Spain.
Background: Headache disorders, particularly migraine, are highly prevalent, but often remain underdiagnosed and undertreated. Artificial intelligence (AI) offers promising applications in diagnosis, prediction of attacks, analysis of neuroimaging and neurophysiology data, and treatment selection. Its use in headache medicine raises ethical, regulatory, and clinical questions, including its impact on the doctor-patient relationship.
View Article and Find Full Text PDFEffects of attractions and social attributes on peoples' usage intention and media dependence towards chatbot: The mediating role of parasocial interaction and emotional support.
BMC Psychol
August 2025
City Culture and Communication College, Suzhou City University, Suzhou, Jiangsu, China.
Purpose: It is important to explore the relationship between humans and chatbots to improve human-robot interaction in the era of artificial intelligence. This study aims to explore the effects of attractions and social attributes of chatbots on users' media dependency and usage intention of chatbots, as well as the role of users' para-social interaction and emotional support gained from chatbots.
Methods: A total of 1,553 responses were collected based on a cross-sectional online survey.
The adoption of large language models (LLMs) in healthcare has garnered significant research interest, yet their performance remains limited due to a lack of domain-specific knowledge, medical reasoning skills, and their unimodal nature, which restricts them to text-only inputs. To address these limitations, we propose MultiMedRes, a multimodal medical collaborative reasoning framework that simulates human physicians' communication by incorporating a learner agent to proactively acquire information from domain-specific expert models. MultiMedRes addresses medical multimodal reasoning problems through three steps i) Inquire: The learner agent decomposes complex medical reasoning problems into multiple domain-specific sub-problems; ii) Interact: The agent engages in iterative "ask-answer" interactions with expert models to obtain domain-specific knowledge; and iii) Integrate: The agent integrates all the acquired domain-specific knowledge to address the medical reasoning problems (e.
View Article and Find Full Text PDF