Background: Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery. Aim: This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them. Methodology: Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties. Results: The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI’s full use in healthcare. Conclusion: Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.

Background: Virtual reality (VR) and augmented reality (AR) are rapidly gaining traction in healthcare, offering transformative opportunities for patient care, medical education, and therapeutic interventions. Despite their potential, challenges remain regarding the implementation and integration of these technologies into existing healthcare practices. Objective: This review aims to explore the current applications of VR and AR in healthcare, particularly focusing on their roles in enhancing patient care and medical training, as well as identifying research gaps that hinder their widespread adoption. Methods: A comprehensive literature search was conducted across 2 primary databases, PubMed, and Google Scholar. The search was restricted to peer-reviewed articles, systematic reviews, meta-analyses, and randomized controlled trials (RCTs) published from 2000 to 2024. Reference lists of included articles were also examined for additional relevant studies. Inclusion criteria focused on empirical studies addressing the use of VR and/or AR in patient care or medical training, while editorial pieces, non-peer-reviewed sources, and unrelated studies were excluded. A total of 17 900 search results were identified on Google Scholar and 300 on PubMed, leading to the inclusion of 89 articles in this review. Results: The findings indicate that VR and AR technologies significantly enhance patient experiences and medical training, providing immersive and interactive environments for learning and practice. However, notable challenges include integration issues with existing electronic health record systems, the need for appropriate implementation models, and a lack of substantial evidence supporting the clinical efficacy of AR-assisted procedures. Conclusion: While VR and AR hold considerable promise in revolutionizing healthcare practices, further research is essential to address existing gaps, particularly regarding implementation strategies, user acceptance, and empirical evaluation of patient outcomes and training effectiveness. Understanding the needs of healthcare professionals and patients will be critical to maximizing the impact of these technologies in clinical settings.

Background and Aims: Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low‐ and middle‐income countries (LMICs).Methods: In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision‐making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies.Results: In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource‐constrained LMICs.Conclusion: While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data‐sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.

Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.

Artificial intelligence technologies were developed to assist authors in bettering the organization and caliber of their published papers, which are both growing in quantity and sophistication. Even though the usage of artificial intelligence tools in particular ChatGPT’s natural language processing systems has been shown to be beneficial in research, there are still concerns about accuracy, responsibility, and transparency when it comes to the norms regarding authorship credit and contributions. Genomic algorithms quickly examine large amounts of genetic data to identify potential disease-causing mutations. By analyzing millions of medications for potential therapeutic benefits, they can quickly and relatively economically find novel approaches to treatment. Researchers from several fields can collaborate on difficult tasks with the assistance of nonhuman writers, promoting interdisciplinary research. Sadly, there are a number of significant disadvantages associated with employing nonhuman authors, including the potential for algorithmic prejudice. Biased data may be reinforced by the algorithm since machine learning algorithms can only be as objective as the data they are trained on. It is overdue that scholars bring forth basic moral concerns in the fight against algorithmic prejudice. Overall, even if the use of nonhuman authors has the potential to significantly improve scientific research, it is crucial for scientists to be aware of these drawbacks and take precautions to avoid bias and limits. To provide accurate and objective results, algorithms must be carefully designed and implemented, and researchers need to be mindful of the larger ethical ramifications of their usage.

The availability and accessibility of neurosurgical care across Africa remains limited despite recent advancements. Overall, Africa accounts for 15% of the global neurosurgical disease burden but has access to less than 1% of neurosurgeons globally. While the number of neurosurgeons has increased in recent decades, huge workforce shortages remain, with the region facing the second-largest neurosurgical deficit. Access to adequate facilities and equipment is also lacking. Barriers like poverty, conflicts, and distance from care centres negatively impact patients’ ability to access services. However, training programs like the World Federation of Neurosurgical Societies Rabat Training Center have contributed to building local capacity. Use of technologies like neuro-endoscopy is expanding access to more cost-effective interventions for conditions such as hydrocephalus. Undergraduate medical education is also seeing a rise in African students interested in neurosurgery. Despite these advancements, workforce shortfalls, inadequate infrastructure, and challenges posed by geopolitical instability continue to hinder the provision of comprehensive neurosurgical care. Limited research and funding discourage experienced surgeons from practicing in their home countries. Increased international collaboration, support for education, and tackling of structural issues are needed to continue strengthening Africa’s neurosurgical capacity and reducing the disease burden. This narrative review aims to provide an overview of the current state of neurosurgery on the continent, highlight achievements, and identify persisting challenges.