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| http://dx.doi.org/10.3389/fpsyg.2023.1266220 | DOI Listing |
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Preparing the 2025 revision of the International Code of Nomenclature of Prokaryotes.
Int J Syst Evol Microbiol
January 2025
Department of Infectious Disease, Institute for Biomedicine, Sahlgrenska Academy, University of Gothenburg, P.O. Box 7193, SE-402 34 Gteborg, Sweden.
The editorial Board of the (ICNP) - the Prokaryotic Code - has compiled already ratified proposed emendations of the ICNP, together with additional editorial changes and clarifications. These were implemented in a draft 2025 revision of the . To comply with Articles 13(b)(4) and 4(d) of the statutes of the International Committee on Systematics of Prokaryotes (ICSP), a public discussion of the document will start on 1 January (or later if required) 2025, to last for 6 months.
View Article and Find Full Text PDFLarge-Scale Validation of the Feasibility of GPT-4 as a Proofreading Tool for Head CT Reports.
Radiology
January 2025
From the Departments of Biomedical Systems Informatics (S.K., Jaewoong Kim, C.H., D.Y.) and Neurology (Joonho Kim, J.Y.), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; Department of Radiology, Central Draft Physical Examination Office of Military Manpower Administration, Daegu, Republic of Korea (D.K.); Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (H.J.S. Y.K., S.J.), and Center for Digital Health (H.J.S., D.Y.), Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea (S.H.L.); Departments of Radiology (M.H.) and Neurology (S.J.L.), Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea; and Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea (D.Y.).
Background The increasing workload of radiologists can lead to burnout and errors in radiology reports. Large language models, such as OpenAI's GPT-4, hold promise as error revision tools for radiology. Purpose To test the feasibility of GPT-4 use by determining its error detection, reasoning, and revision performance on head CT reports with varying error types and to validate its clinical utility by comparison with human readers.
View Article and Find Full Text PDFThe sociolinguistic foundations of language modeling.
Front Artif Intell
January 2025
Department of Linguistics and Communication, University of Birmingham, Birmingham, United Kingdom.
In this article, we introduce a sociolinguistic perspective on language modeling. We claim that language models in general are inherently modeling , and we consider how this insight can inform the development and deployment of language models. We begin by presenting a technical definition of the concept of a variety of language as developed in sociolinguistics.
View Article and Find Full Text PDFPost-processing enhances protein secondary structure prediction with second order deep learning and embeddings.
Comput Struct Biotechnol J
January 2025
University of Cyprus, Department of Computer Science, Nicosia, Cyprus.
Protein Secondary Structure Prediction (PSSP) is regarded as a challenging task in bioinformatics, and numerous approaches to achieve a more accurate prediction have been proposed. Accurate PSSP can be instrumental in inferring protein tertiary structure and their functions. Machine Learning and in particular Deep Learning approaches show promising results for the PSSP problem.
View Article and Find Full Text PDFResponsible Design, Integration, and Use of Generative AI in Mental Health.
JMIR Ment Health
January 2025
The Samueli Initiative for Responsible AI in Medicine, Tel Aviv University, Tel Aviv, Israel.
Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet responsible use requires ethical consideration and deliberation and perhaps even governance. This is the first published theme issue focused on responsible GenAI in mental health. It brings together evidence and insights on GenAI's capabilities, such as emotion recognition, therapy-session summarization, and risk assessment, while highlighting the sensitive nature of mental health data and the need for rigorous validation.
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