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| http://dx.doi.org/10.1007/s00464-024-11132-y | DOI Listing |
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Faster and better than a physician?: Assessing diagnostic proficiency of ChatGPT in misdiagnosed individuals with neuromyelitis optica spectrum disorder.
J Neurol Sci
December 2024
The University of Texas Southwestern Medical Center, Department of Neurology, Neuroinnovation Program, Multiple Sclerosis & Neuroimmunology Imaging Program, Dallas, TX, USA; The University of Texas Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, USA. Electronic address:
Background: Neuromyelitis optica spectrum disorder (NMOSD) is a commonly misdiagnosed condition. Driven by cost-consciousness and technological fluency, distinct generations may gravitate towards healthcare alternatives, including artificial intelligence (AI) models, such as ChatGPT (Generative Pre-trained Transformer). Our objective was to evaluate the speed and accuracy of ChatGPT-3.
View Article and Find Full Text PDFRapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.
Spectrochim Acta A Mol Biomol Spectrosc
December 2024
College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected.
View Article and Find Full Text PDFThe aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data.
BMC Med Inform Decis Mak
December 2024
Klinikum Stuttgart, Stuttgart Cancer Center - Tumorzentrum Eva Mayr-Stihl DE, Kriegsbergstraße 60, Stuttgart, D-70174, Germany.
Background: Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health state evolution.
View Article and Find Full Text PDFPhysician vs. AI-generated messages in urology: evaluation of accuracy, completeness, and preference by patients and physicians.
World J Urol
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Department of Urology, Baldwin Park Medical Center, Kaiser Permanente, 1011 Baldwin Park Blvd., Baldwin Park, CA, 91706, USA.
Purpose: To evaluate the accuracy, comprehensiveness, empathetic tone, and patient preference for AI and urologist responses to patient messages concerning common BPH questions across phases of care.
Methods: Cross-sectional study evaluating responses to 20 BPH-related questions generated by 2 AI chatbots and 4 urologists in a simulated clinical messaging environment without direct patient interaction. Accuracy, completeness, and empathetic tone of responses assessed by experts using Likert scales, and preferences and perceptions of authorship (chatbot vs.
Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning.
Nurs Rep
December 2024
Mental Health and Specialist Services, West Moreton Health, Brisbane, QLD 4076, Australia.
Background: Optimum efficiency and responsiveness to callers of mental health helplines can only be achieved if call priority is accurately identified. Currently, call operators making a triage assessment rely heavily on their clinical judgment and experience. Due to the significant morbidity and mortality associated with mental illness, there is an urgent need to identify callers to helplines who have a high level of distress and need to be seen by a clinician who can offer interventions for treatment.
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