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Article
Health Care Sciences & Services
Yoonseo Park et al.
Summary: This study identified stakeholders and major issues in the ecosystem of the digital health care industry through network analysis. The major issues include telemedicine, data, and health care business. Developing an appropriate digital health care fee model is essential for the development and institutionalization of digital health care.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Chemistry, Multidisciplinary
Yanru Lyu et al.
Summary: The use of artificial intelligence (AI) in art creation has become a mainstream phenomenon. This study investigates the differences in interactions with a text-to-image system between artists and non-artists. The experiment revealed significant differences in creation actions and attitudes towards AI between the two groups, while the technology itself blurred the differences in perception caused by the creator's artistic experience.
APPLIED SCIENCES-BASEL
(2022)
Editorial Material
Health Care Sciences & Services
Jakob Nikolas Kather et al.
Summary: The GLIDE model, a state-of-the-art text-to-image generative AI model, shows promising representations in cancer research and pathology, but lacks useful representations in radiology data.
NPJ DIGITAL MEDICINE
(2022)
Review
Health Care Sciences & Services
Malvika Sharma et al.
Summary: This study examines the implementation of AI in healthcare practice and finds that current research focuses on clinical care and technical aspects, with limited attention to the implementation process.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Materials Science, Multidisciplinary
Markus J. Buehler
Summary: In recent years, various image generation methods have been developed, such as DALL-E 2, Imagen, and Stable Diffusion. However, the application of these methods in materials design has not been explored. This article uses a trained Stable Diffusion model as an experimental system to investigate its capability to generate novel material designs, specifically in the context of 3D material architectures. The authors propose a series of methods to translate 2D representations into 3D data and demonstrate the potential to generate diverse material patterns and designs using human-readable language as input. Physical samples are created using additive manufacturing, and the material properties are assessed through a coarse-grained particle simulation approach. The case studies show that starting with images, complex materials can be generated, which can be tuned to meet specific requirements.
OXFORD OPEN MATERIALS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Youbao Tang et al.
Summary: The interpretation of medical images is a complex cognitive process that requires accurate understanding of normal anatomy and pathology. This study introduces a deep disentangled generative model (DGM) that can generate disease residue maps and realistic normal CXR images, improving diagnosis efficiency and clinical applications for radiologists.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Mathematics
Giorgio Ciano et al.
Summary: This study proposes a novel multi-stage generation algorithm based on Generative Adversarial Networks for multi-organ segmentation of X-ray images, with the function of data augmentation. The method simplifies the process, is suitable for small datasets, and shows promising results in chest radiographic image segmentation.
Article
Health Care Sciences & Services
August DuMont Schuette et al.
Summary: Research suggests that using Generative Adversarial Networks (GANs) to create synthetic medical imaging datasets may help address privacy concerns in medical research while protecting sensitive personal information. Evaluation of the quality of synthetic data generated by two GAN models and further predictive performance analysis of radiology findings show that synthetic data performs better with reduced numbers of classes, and visual resolution significantly impacts radiologists' ability to differentiate between synthetic and real images.
NPJ DIGITAL MEDICINE
(2021)
Article
Computer Science, Hardware & Architecture
Ian Goodfellow et al.
COMMUNICATIONS OF THE ACM
(2020)
