The Altmetric Attention Score: how science tries to meet social media

Measuring the impact of scientific publications and publication extenders: examples of novel approaches

Different stakeholders, such as authors, research institutions, and healthcare professionals (HCPs) may determine the impact of peer-reviewed publications in different ways. Commonly-used measures of research impact, such as the Journal Impact Factor or the H-index, are not designed to evaluate the impact of individual articles. They are heavily dependent on citations, and therefore only measure impact of the overall journal or researcher respectively, taking months or years to accrue. The past decade has seen the development of article-level metrics (ALMs), that measure the online attention received by an individual publication in contexts including social media platforms, news media, citation activity, and policy and patent citations. These new tools can complement traditional bibliometric data and provide a more holistic evaluation of the impact of a publication. This commentary discusses the need for ALMs, and summarizes several examples - PlumX Metrics, Altmetric, the Better Article Metrics score, the EMPIRE Index, and scite. We also discuss how metrics may be used to evaluate the value of "publication extenders" - educational microcontent such as animations, videos and plain-language summaries that are often hosted on HCP education platforms. Publication extenders adapt a publication's key data to audience needs and thereby extend a publication's reach. These new approaches have the potential to address the limitations of traditional metrics, but the diversity of new metrics requires that users have a keen understanding of which forms of impact are relevant to a specific publication and select and monitor ALMs accordingly.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.