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Introduction Large language model (LLM)-linked chatbots are being increasingly applied in healthcare due to their impressive functionality and public availability. Studies have assessed the ability of LLM-linked chatbots to provide accurate clinical advice. However, the methods applied in these Chatbot Assessment Studies are inconsistent due to the lack of reporting standards available, which obscures the interpretation of their study findings. This protocol outlines the development of the Chatbot Assessment Reporting Tool (CHART) reporting guideline.Methods and analysis The development of the CHART reporting guideline will consist of three phases, led by the Steering Committee. During phase one, the team will identify relevant reporting guidelines with artificial intelligence extensions that are published or in development by searching preprint servers, protocol databases, and the Enhancing the Quality and Transparency of health research Network. During phase two, we will conduct a scoping review to identify studies that have addressed the performance of LLM-linked chatbots in summarising evidence and providing clinical advice. The Steering Committee will identify methodology used in previous Chatbot Assessment Studies. Finally, the study team will use checklist items from prior reporting guidelines and findings from the scoping review to develop a draft reporting checklist. We will then perform a Delphi consensus and host two synchronous consensus meetings with an international, multidisciplinary group of stakeholders to refine reporting checklist items and develop a flow diagram.Ethics and dissemination We will publish the final CHART reporting guideline in peer-reviewed journals and will present findings at peer-reviewed meetings. Ethical approval was submitted to the Hamilton Integrated Research Ethics Board and deemed “not required” in accordance with the Tri-Council Policy Statement (TCPS2) for the development of the CHART reporting guideline (#17025).Registration This study protocol is preregistered with Open Science Framework: https://doi.org/10.17605/OSF.IO/59E2Q.

Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To the best of our knowledge, this is the first work in this domain. Applying deep learning techniques for semantic segmentation tasks in real-world scenarios has its own challenges, especially the difficulty for models to effectively learn from complex backgrounds while focusing on simpler objects of interest. This challenge is particularly evident in the task of detecting seepages in levee systems, where the fault is relatively simple compared to the complex and varied background. We addressed this problem by introducing negative images and a controlled transfer learning approach for semantic segmentation for accurate seepage segmentation in levee systems.

This document provides guidance on the use of artificial intelligence (AI), natural language processing (NLP), and language models (LLMs) in scholarly publishing. It emphasizes the importance of transparency, responsibility, and accountability in the use of AI to ensure the integrity and quality of published research. Authors are advised to disclose their use of AI tools and provide detailed information about their usage. Peer reviewers are cautioned against using AI tools that may compromise confidentiality, and editors are responsible for effectively and transparently implementing AI tools. Efforts should be made to bridge the digital divide and prevent disparities in access to AI technologies. Data protection measures, intellectual property rights, and addressing AI-induced academic misconduct are also highlighted as important considerations. [Extracted from the article]

Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To the best of our knowledge, this is the first work in this domain. Applying deep learning techniques for semantic segmentation tasks in real-world scenarios has its own challenges, especially the difficulty for models to effectively learn from complex backgrounds while focusing on simpler objects of interest. This challenge is particularly evident in the task of detecting seepages in levee systems, where the fault is relatively simple compared to the complex and varied background. We addressed this problem by introducing negative images and a controlled transfer learning approach for semantic segmentation for accurate seepage segmentation in levee systems. [ABSTRACT FROM AUTHOR]

The article offers information on the 10th International Congress on Peer Review and Scientific Publication and its call for abstract submissions. Topics include the evolving challenges in scientific publication such as AI and reproducibility issues, the Congress's emphasis on novel, data-driven research to improve peer review, and its encouragement of interdisciplinary studies across diverse scientific fields.

This article explores the use of artificial intelligence (AI) in scholarly publishing and the potential benefits and challenges it presents. While AI technologies like machine learning and natural language processing can streamline research and publishing, concerns about bias, inaccuracies, and ethical issues arise. The article emphasizes the need for guidance and best practices to ensure responsible use of AI in research and publication, addressing transparency, responsibility, and accountability. It also highlights the importance of addressing data privacy, authorship attribution, intellectual property rights, and plagiarism detection. Access to advanced AI technologies is not uniform worldwide, and ensuring access in scholarly publishing is crucial to prevent disparities in knowledge creation and dissemination. The article concludes by providing recommendations for authors on the use of AI tools in manuscript preparation, emphasizing transparency and accountability. [Extracted from the article]

Sanford Drob's Kabbalistic Visions: C. G. Jung and Jewish Mysticism examines the parallels between Kabbalistic spiritual practice and Jungian analytic work, including the conjunction of such opposites as male and female and especially of light and dark. The Kabbalah is presented in its historical evolution from an exoteric Jewish tradition to an esoteric interior process that is mirrored in Jungian analysis. This enables us to understand how Jung's late visions were rooted in his controversial but evolving attitude toward Jewish psychology, resulting in the widely noted deepening of his last major works. [ABSTRACT FROM AUTHOR]

An editorial provides comprehensive guidance for authors, researchers, reviewers, and editors on the transparent and accountable reporting of artificial intelligence (AI) use in medical research and scholarly publication. It emphasizes the importance of clear documentation, adherence to reporting guidelines, and addressing potential biases and inaccuracies associated with AI models.

The article focuses on advancing equity within a medical publishing network, specifically discussing self-reported demographics of editors and editorial board members. Topics discussed include efforts to ensure diverse representation, the standardized schema for demographic reporting, and initiatives for improving equity and inclusion in medical publishing.

The article discusses the importance of inclusive language in medical and scientific publications when describing people. It emphasizes the need to avoid noninclusive language that may convey bias and instead use terminology that promotes clarity, inclusiveness, respect, and fairness. The article also mentions the draft guidance on reporting gender, sex, gender identity, sexual orientation, and age in medical and scientific publications, which provides recommendations for accurate and respectful language usage. The guidance is open for public review and comment until September 30, 2024, and will be revised and published in the AMA Manual of Style. [Extracted from the article]

The article discusses the JAMA Summit that was held in Chicago, U.S. in October 2023. Topics discussed include the challenges and opportunities to improve the design and conduct of randomized clinical trials, with a focus on making them more responsive to the needs in clinical practice. Attendees included researchers, funders, regulators, patient advocates, policymakers, innovators, and industry representatives.