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Assessing online chat-based artificial intelligence models for weight loss recommendation appropriateness and bias in the presence of guideline incongruence.
Int J Obes (Lond)
January 2025
Department of Gastroenterology and Hepatology, University of Illinois College of Medicine, Peoria, IL, USA.
Background And Aim: Managing obesity requires a comprehensive approach that involves therapeutic lifestyle changes, medications, or metabolic surgery. Many patients seek health information from online sources and artificial intelligence models like ChatGPT, Google Gemini, and Microsoft Copilot before consulting health professionals. This study aims to evaluate the appropriateness of the responses of Google Gemini and Microsoft Copilot to questions on pharmacologic and surgical management of obesity and assess for bias in their responses to either the ADA or AACE guidelines.
View Article and Find Full Text PDFUsing metabolic parameters to inform emergency care referral in a patient with a hyperglycemic induced acute kidney injury: a case report.
Physiother Theory Pract
January 2025
APReCIAT Lab - Department of Physical Therapy, University of Miami, Coral Gables, Florida, USA.
Objective: To describe the clinical reasoning and use of the American Diabetes Association (ADA) blood glucose and exercise guidelines in the face of an emerging acute glycemic crisis for a patient with type 2 diabetes mellitus receiving physical therapy for chronic ankle instability and fibromyalgia.
Case Description: Assessment of the patient's baseline blood glucose and ketone urinalysis revealed hyperglycemia and ketonuria, respectively. Shortly after testing, the patient became nauseous and vomited.
Derivation and Validation of D-RISK: An Electronic Health Record-Driven Risk Score to Detect Undiagnosed Dysglycemia in Clinical Practice.
Diabetes Care
January 2025
Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ.
Objective: We derive and validate D-RISK, an electronic health record (EHR)-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (prediabetes + diabetes) in clinical practice.
Research Design And Methods: We used retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backward selection was used to predict dysglycemia (HbA1c ≥5.
Evaluation of ChatGPT-4 Performance in Answering Patients' Questions About the Management of Type 2 Diabetes.
Sisli Etfal Hastan Tip Bul
December 2024
Department of Endocrinology and Metabolic Diseases, Ankara Training and Research Hospital, Ankara, Türkiye.
Objectives: Type 2 diabetes mellitus is a disease with a rising prevalence worldwide. Person-centered treatment factors, including comorbidities and treatment goals, should be considered in determining the pharmacological treatment of type 2 diabetes. ChatGPT-4 (Generative Pre-trained Transformer), a large language model, holds the potential performance in various fields, including medicine.
View Article and Find Full Text PDFAzSLD: Azerbaijani sign language dataset for fingerspelling, word, and sentence translation with baseline software.
Data Brief
February 2025
ADA University, Baku, Azerbaijan.
Advancements in sign language processing technology hinge on the availability of extensive, reliable datasets, comprehensive instructions, and adherence to ethical guidelines. To facilitate progress in gesture recognition and translation systems and to support the Azerbaijani sign language community we present the Azerbaijani Sign Language Dataset (AzSLD). This comprehensive dataset was collected from a diverse group of sign language users, encompassing a range of linguistic parameters.
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