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| http://dx.doi.org/10.3389/ijph.2022.1605257 | DOI Listing |
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The Venus score for the assessment of the quality and trustworthiness of biomedical datasets.
BioData Min
January 2025
Fondazione Bruno Kessler, Trento, Italy.
Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets.
View Article and Find Full Text PDFEarly childhood caries risk prediction using machine learning approaches in Bangladesh.
BMC Oral Health
January 2025
Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
Background: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs.
Methods: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh.
Exploring artificial intelligence for differentiating early syphilis from other skin lesions: a pilot study.
BMC Infect Dis
January 2025
Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia.
Background: Early diagnosis of syphilis is vital for its effective control. This study aimed to develop an Artificial Intelligence (AI) diagnostic model based on radiomics technology to distinguish early syphilis from other clinical skin lesions.
Methods: The study collected 260 images of skin lesions caused by various skin infections, including 115 syphilis and 145 other infection types.
The TRIPOD-LLM reporting guideline for studies using large language models.
Nat Med
January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.
View Article and Find Full Text PDFA generalist medical language model for disease diagnosis assistance.
Nat Med
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State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
The delivery of accurate diagnoses is crucial in healthcare and represents the gateway to appropriate and timely treatment. Although recent large language models (LLMs) have demonstrated impressive capabilities in few-shot or zero-shot learning, their effectiveness in clinical diagnosis remains unproven. Here we present MedFound, a generalist medical language model with 176 billion parameters, pre-trained on a large-scale corpus derived from diverse medical text and real-world clinical records.
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