AIMC Topic: Uganda

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Are malaria rapid diagnostic test results stable over time to support verification of surveillance data?

Malaria journal
BACKGROUND: Rapid diagnostic tests (RDTs) have improved malaria case management by enabling point-of-care confirmation of infection, particularly in low-resource settings. In addition to clinical use, RDT results recorded in health facility registers...

Strengthening ethics review of the development of artificial intelligence (AI) systems in health research: a guide for research ethics committees in Uganda.

BMC medical ethics
INTRODUCTION: The ability of artificial intelligence (AI) to analyze data in real-time and improve patients' diagnosis has led to a rapid growth of AI- health research in Uganda. Yet, there are no national guidelines on how to conduct AI-research in ...

"I can no longer give take-home exams": Health professionals educators' experiences and perceptions regarding the use of artificial intelligence in health professions education in Uganda.

BMC medical education
INTRODUCTION: Artificial intelligence (AI) tools offer immense opportunities and challenges for medical education. However, there is limited information about the use of AI among health professional educators, particularly in low- and middle-income c...

Evaluating the performance of an artificial intelligence-based electronic reader for malaria rapid diagnostic tests across Benin, Côte d'Ivoire, Nigeria and Uganda.

Malaria journal
BACKGROUND: The introduction of malaria rapid diagnostic tests (RDTs) has expanded the parasitological confirmation of malaria at all levels of health systems in sub-Saharan Africa, improving case management and surveillance. However, concerns persis...

Assessing the performance of a point-of-need diagnostic algorithm in rapid detection of peripheral lymph node tuberculosis (Mobile-TB-Lab): a diagnostic evaluation study protocol.

BMJ open
INTRODUCTION: Early and accurate diagnosis of tuberculosis (TB) is central to ensuring the proper treatment and curbing the transmission of the disease. Despite the significant burden, the diagnosis of peripheral lymph node(LN)TB, the most prevalent ...

Mother: a maternal online technology for health care dataset.

BMC research notes
OBJECTIVES: These data enable the development of both textual and speech based conversational machine learning models that can be used by expectant mothers to provide answers to challenges they face during the different trimesters of their pregnancy....

Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.

AIDS (London, England)
OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 target of 2030, especially among adolescents with HIV (AWHIV). This study sought to develop a model ...

Mental health phenotypes of well-controlled HIV in Uganda.

Frontiers in public health
INTRODUCTION: The phenotypic expression of mental health (MH) conditions among people with HIV (PWH) in Uganda and worldwide are heterogeneous. Accordingly, there has been a shift toward identifying MH phenotypes using data-driven methods capable of ...

Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

BMC emergency medicine
BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these s...

Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study.

PloS one
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, i...