Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review.

Objectives: The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations.

Materials And Methods: The Appraisal of Guidelines, Research, and Evaluation II tool was used to assess the quality of guidelines based on 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence.

CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.

Background: Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent.

Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under-fives using demographic and health survey insights.

Background: A dearth of studies showed that infectious diseases cause the majority of deaths among under-five children. Worldwide, Acute Respiratory Infection (ARI) continues to be the second most frequent cause of illness and mortality among children under the age of five. The paramount disease burden in developing nations, including Ethiopia, is still ARI.

Guidelines and Standard Frameworks for AI in Medicine: Protocol for a Systematic Literature Review.

Background: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI.

Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and Code Perspective.

The interest in the application of AI in medicine has intensely increased over the past decade with most of the changes in the past five years. Most recently, the application of deep learning algorithms in prediction and classification of cardiovascular diseases (CVD) using computed tomography (CT) images showed promising results. The notable and exciting advancement in this area of study is, however, associated with different challenges related to the findability (F), accessibility(A), interoperability(I), reusability(R) of both data and source code.

Disparities in Regional Publication Trends on the Topic of Artificial Intelligence in Biomedical Science Over the Last Five Years: A Bibliometric Analysis.

Bibliometric analysis is a scientific method that allows researchers to explore the current trend in a certain research area using citation information. This study aims to provide a meta-view of artificial intelligence studies focused on biomedicine in the last five years, which will provide an insight into current trends and future research directions. Besides the observation of increased publication rates in the area of AI in biomedicine, the results indicate a lower contribution from and a sparser network connectivity of countries with limited resources.

Treatment Satisfaction and Its Associated Factors of Dolutegravir Based Regimen in a Resource Limited Setting.

Introduction: Treatment satisfaction is patient's evaluation of the process of taking the medication and its use. Currently dolutegravir based regimen is first-line agent for treatment of human immunodeficiency virus. But evidence is scarce regarding treatment satisfaction.

Healthcare providers' acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model.

Background: In almost all lower and lower middle-income countries, the healthcare system is structured in the customary model of in-person or face to face model of care. With the current global COVID-19 pandemics, the usual health care service has been significantly altered in many aspects. Given the fragile health system and high number of immunocompromised populations in lower and lower-middle income countries, the economic impacts of COVID-19 are anticipated to be worse.

Healthcare providers' digital competency: a cross-sectional survey in a low-income country setting.

Background: Healthcare providers across all clinical practice settings are progressively relying and adapting information communication technologies to perform their professional activities. In this era of technology, healthcare providers especially in lower income countries should have at least basic digital competency if a successful application of technology is to be achieved. The aim of this study was to assess digital competency of healthcare providers among seven public health centers in North-West Ethiopia.

E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey.

Background: Chronic patients persistently seek for health information on the internet for medication information seeking, nutrition, disease management, information regarding disease preventive actions and so on. Consumers ability to search, find, appraise and use health information from the internet is known as eHealth literacy skill. eHealth literacy is a congregate set of six basic skills (traditional literacy, health literacy, information literacy, scientific literacy, media literacy and computer literacy).

Validation of the Ethiopian Version of eHealth Literacy Scale (ET-eHEALS) in a Population with Chronic Disease.

Background: Although the measurement scale developed by Norman and Skinner is the widely used scale to assess consumers' eHealth literacy, translating and validating the scale for the language of the target population under consideration is necessary. Amharic is the official national language of Ethiopia, with 29.3% of native speakers.

Internet use and eHealth literacy among health-care professionals in a resource limited setting: a cross-sectional survey.

Background: Health-care professionals should be able to identify and use reputable health care-information sources from the Internet and other relevant sources of information, in order to make good medical decisions. The level in health professional eHealth literacy and the extent of Internet use in a resource-constrained setting is not well documented. The aim of this study was to assess the extent of Internet use and eHealth literacy among a cross section of health-care professionals at the University of Gondar Comprehensive Specialized Hospital, northwest Ethiopia.