Public COVID-19 vaccination acceptance: A narrative review of correlated factors

Following the discovery of the first instances of COVID-19 in nations and the subsequent announcement of a “pandemic” by WHO, worldwide efforts to identify efficient methods to combat COVID-19 began. One of the most effective solutions is to carry out widespread vaccination against the virus. Despite this, some members of the community refuse to be vaccinated. The present paper reviews the potential causes and factors correlating with people's hesitation to receive COVID-19 vaccines. This article is a narrative review paper. We searched PubMed, Scopus, and Web of Science databases using COVID-19, Vaccine, Acceptance, and Hesitancy keywords. Qualitative content analysis was performed and associated predictors with public vaccination acceptance were identified. According to the study,  hesitation in receiving COVID-19 vaccines, regardless of the countries, is significant among females, lower ages, lower education level, doubt about efficacy, and concerns about the safety of the vaccines, history of not receiving vaccines, especially the influenza vaccine, distrust of regional or national health officials, low level of health literacy and lack of information, fear of side effects and other complications, doubt of pharmaceutical companies and fear of lobbying, presence of chronic underlying diseases and comorbidities, lower socioeconomic status and racial or religious minorities. According to the results, several factors can influence individuals' uncertainty about COVID-19 vaccines. Given the importance of vaccinating the majority of the community to achieve mass immunity, healthcare systems should consider the vaccine acceptance rate to be a vital and substantial factor.Keywords: COVID-19; Vaccination Refusal; Review

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt