AIMC Topic: Cost-Benefit Analysis
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The feasibility and cost-effectiveness of implementing mobile low-dose computed tomography with an AI-based diagnostic system in underserved populations.
BMC cancer
Feb 25, 2025
BACKGROUND: Low-dose computed tomography (LDCT) significantly increases early detection rates of lung cancer and reduces lung cancer-related mortality by 20%. However, many significant screening barriers remain. This study conduct an initial feasibil...
Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications.
Journal of chemical information and modeling
Feb 11, 2025
Pretrained language models have demonstrated strong capability and versatility in natural language processing (NLP) tasks, and they have important applications in optoelectronics research, such as data mining and topic modeling. Many language models ...
Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP.
Scientific reports
Feb 9, 2025
To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed...
Cost-effectiveness of AI-based diabetic retinopathy screening in nationwide health checkups and diabetes management in Japan: A modeling study.
Diabetes research and clinical practice
Jan 30, 2025
AIMS: We evaluated the cost-effectiveness of artificial intelligence (AI)-based diabetic retinopathy (DR) screening in Japan. This evaluation compared the simultaneous introduction of AI in nationwide health checkups, namely "specific health check-up...
AI-generated cancer prevention influencers can target risk groups on social media at low cost.
European journal of cancer (Oxford, England : 1990)
Jan 18, 2025
BACKGROUND: This study explores the potential of Artificial Intelligence (AI)-generated social media influencers to disseminate cancer prevention messages. Utilizing a Generative AI (GenAI) application, we created a virtual persona, "Wanda", to promo...
Design of a Cost-Effective Ultrasound Force Sensor and Force Control System for Robotic Extra-Body Ultrasound Imaging.
Sensors (Basel, Switzerland)
Jan 15, 2025
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering...
Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial.
Journal of medical Internet research
Jan 14, 2025
BACKGROUND: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment meth...
Cost-effectiveness of a machine learning risk prediction model (LungFlag) in the selection of high-risk individuals for non-small cell lung cancer screening in Spain.
Journal of medical economics
Jan 8, 2025
OBJECTIVE: The LungFlag risk prediction model uses individualized clinical variables to identify individuals at high-risk of non-small cell lung cancer (NSCLC) for screening with low-dose computed tomography (LDCT). This study evaluates the cost-effe...
Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study.
Waste management (New York, N.Y.)
Jan 8, 2025
This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from ...
The use of machine learning for the prediction of response to follow-up in spine registries.
International journal of medical informatics
Jan 3, 2025
BACKGROUND: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the cha...
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