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Artificial Intelligence-Prompted Explanations of Common Primary Care Diagnoses.
PRiMER
September 2024
Morsani College of Medicine, University of South Florida, Tampa, FL | Department of Family Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL.
Background: Artificial intelligence (AI)-generated explanations about medical topics may be clearer and more accessible than traditional evidence-based sources, enhancing patient understanding and autonomy. We evaluated different AI explanations for patients about common diagnoses to aid in patient care.
Methods: We prompted ChatGPT 3.
Online Depression Communities as a Complementary Approach to Improving the Attitudes of Patients With Depression Toward Medication Adherence: Cross-Sectional Survey Study.
J Med Internet Res
November 2024
Department of Marketing, School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China.
Background: Lack of adherence to prescribed medication is common among patients with depression in China, posing serious challenges to the health care system. Online health communities have been found to be effective in enhancing patient compliance. However, empirical evidence supporting this effect in the context of depression treatment is absent, and the influence of online health community content on patients' attitudes toward medication adherence is also underexplored.
View Article and Find Full Text PDFPoor Representation of Rural Counties of the United States in Some Measures of Consumer Broadband.
Telemed Rep
October 2024
Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
Introduction: Telehealth has the potential to mitigate the lack of health care access in rural and underserved communities; however, telehealth is only viable where sufficiently high-speed internet broadband is available to patients. Existing broadband data sets may not accurately reflect the state of broadband, particularly in rural communities. We examined consumer internet speed test data from two organizations to see if the number of tests per 1,000 residents varied across county-level rurality.
View Article and Find Full Text PDFUsing Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.
J Med Internet Res
September 2024
Doctorpresso, Seoul, Republic of Korea.
Background: Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs).
View Article and Find Full Text PDFAssessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts.
JMIR Med Educ
July 2024
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse.
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