Role of artificial intelligence in digital pathology for gynecological cancers

Wang, Ya-Li; Gao, Song; Xiao, Qian; Li, Chen; Grzegorzek, Marcin; Zhang, Ying-Ying; Li, Xiao-Han; Kang, Ye; Liu, Fang-Hua; Huang, Dong-Hui; Gong, Ting-Ting; Wu, Qi-Jun

doi:10.1016/j.csbj.2024.03.007

Cited by 5 publications

(1 citation statement)

References 60 publications

(86 reference statements)

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“…(44,45,46,47) • Inteligencia Artificial (IA): Aporta capacidades como el aprendizaje automático, el procesamiento del lenguaje natural y la visión por computadora, que pueden automatizar tareas, predecir comportamientos y personalizar experiencias. (48,49,50,51,52,53) • Realidad Aumentada (AR): Permite superponer información digital en el mundo real, lo que se utiliza en capacitación, mantenimiento, diseño de productos y experiencias de cliente inmersivas. (54,55,56,57,58,59) •…”

Section: Introductionunclassified

Bibliometric analysis of the main applications of digital technologies to business management

Gómez-Cano,

Sánchez-Castillo,

Eslava-Zapata

2024

Data and Metadata

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En la era digital actual, las tecnologías de la información han revolucionado la forma en que las empresas gestionan sus operaciones y estrategias comerciales. La aplicación de estas tecnologías en la gestión de negocios ha demostrado impactos significativos en diversos sectores. El objetivo principal fue analizar la producción científica relacionada con las principales aplicaciones de las tecnologías digitales a la gestión de negocios. EL paradigma de la investigación fue mixto mediante el desarrollo de un estudio bibliométrico y un análisis temático de fuentes relevantes. Se utilizó la base de datos SCOPUS durante el período 2000 – 2024. Se obtuvieron un total de 85 investigaciones. El comportamiento de las investigaciones se comportó de forma heterogénea, mientras que a partir del año 2019 experimentó un crecimiento notable con un pico máximo en el año 2023 de 24 investigaciones. El análisis temático corroboró la importancia de la transformación digital para la gestión de negocios y el rol clave que ejerce la introducción diseñada de las tecnologías digitales. Los hallazgos permiten afirmar que se trata de un campo heterogéneo, influenciado por diversas disciplinas y en vías de consolidación, debido al abanico de potencialidades que ofrece.

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Section: Introductionunclassified

Bibliometric analysis of the main applications of digital technologies to business management

Gómez-Cano,

Sánchez-Castillo,

Eslava-Zapata

2024

Data and Metadata

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Synthetic Genitourinary Image Synthesis via Generative Adversarial Networks: Enhancing AI Diagnostic Precision

Van Booven,

Chen,

Malpani

et al. 2024

Preprint

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In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality synthetic images of rare or underrepresented GU tissues. We hypothesized that augmenting the training data of computational pathology models with these GAN-generated images, validated through pathologist evaluation and quantitative similarity measures, would significantly enhance model performance in tasks such as tissue classification, segmentation, and disease detection. To test this hypothesis, we employed a GAN model to produce synthetic images of eight different GU tissues. The quality of these images was rigorously assessed using a Relative Inception Score (RIS) of 17.2 and a Frechet Inception Distance (FID) that stabilized at 120, metrics that reflect the visual and statistical fidelity of the generated images to real histopathological images. Additionally, the synthetic images received an 80% approval rating from board-certified pathologists, further validating their realism and diagnostic utility. We used an alternative Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA) to assess quality in prostate tissue. This allowed us to make a comparison between original and synthetic data in the context of features, which were further validated by the pathologist's evaluation. Future work will focus on implementing a deep learning model to evaluate the performance of the augmented datasets in tasks such as tissue classification, segmentation, and disease detection. This will provide a more comprehensive understanding of the utility of GAN-generated synthetic images in enhancing computational pathology workflows. This study not only confirms the feasibility of using GANs for data augmentation in medical image analysis but also highlights the critical role of synthetic data in addressing the challenges of dataset scarcity and imbalance. Future work will focus on refining the generative models to produce even more diverse and complex tissue representations, potentially transforming the landscape of medical diagnostics with AI-driven solutions.

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A bibliometric analysis of the advance of artificial intelligence in medicine (Preprint)

lin,

Lin,

Lin

et al. 2024

Preprint

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BACKGROUND The integration of artificial intelligence (AI) into medicine has ushered an era of unprecedented innovation, with substantial impacts on healthcare delivery and patient outcomes. OBJECTIVE it is essential to comprehend the current state of development, primary research focal points, and to identify key contributors and their relationships in the application of AI in medicine through bibliometric analysis. METHODS We employed the Web of Science Core Collection as our primary database and conducted a literature search spanning from January 2019 to December 2023.VOSviewer and R-bibliometrix were performed to conduct bibliometric analysis and network visualization, including the number of publications, countries, journals, citations, authors and keywords. RESULTS A total of 1811 publications on research for artificial intelligence in medicine were released across 565 journals by 12376 authors affiliated with 3583 institutions from 97 countries. The United States emerged as the leading producer of scholarly works, exerting significant influence in this domain. Harvard Medical School exhibited the highest publication count among all institutions. The JOURNAL OF MEDICAL INTERNET RESEARCH attained the highest H-index (H-index=19), the most significant publication count (NP=76), and total citations (NC=1495). Among the keywords, four clusters were identified, encompassing the application of AI in digital health, COVID-19 and ChatGPT, precision medicine, epidemiology, and public health. "Outcomes" and "Risk" demonstrated a notable upward trend, indicating the utilization of AI in engaging with clinicians and patients to discuss patients' health condition risks, foreshadowing future research focal points. CONCLUSIONS Our bibliometric analysis delved into the advancements, focal points, and cutting-edge areas within the field of artificial intelligence in medicine, revealing potential future research opportunities. Research on artificial intelligence in medicine is rapidly progressing, as evidenced by a consistent increase in publications on the topic since 2019. Simultaneously, we identified leading countries, institutions, and scholars in the field and conducted an analysis of journals and representative literature. This study equips researchers with the necessary information to comprehend the current state, collaborative networks, and primary research focal points within the field. Furthermore, our findings propose a set of recommendations for future research.

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Role of artificial intelligence in digital pathology for gynecological cancers

Cited by 5 publications

References 60 publications

Bibliometric analysis of the main applications of digital technologies to business management

Bibliometric analysis of the main applications of digital technologies to business management

Synthetic Genitourinary Image Synthesis via Generative Adversarial Networks: Enhancing AI Diagnostic Precision

A bibliometric analysis of the advance of artificial intelligence in medicine (Preprint)

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