Artificial intelligence for acute appendicitis management: Implications, challenges, and recommendations for Nigeria
DOI:
https://doi.org/10.52225/narrarev.v1i3.16Keywords:
Artificial intelligence, acute appendicitis, developing countries, leadership, NigeriaAbstract
Artificial intelligence (AI) is increasingly transforming the diagnosis and management of acute appendicitis in high-income countries, with growing evidence demonstrating improved diagnostic accuracy, risk stratification, and clinical decision support across both adult and pediatric populations. Recent advances range from clinical- and laboratory-based machine learning models to explainable, multi-task systems and fully automated imaging pipelines. The aim of this study was to examine the current landscape of AI applications for acute appendicitis and critically evaluates their relevance, feasibility, and translational potential within the Nigerian healthcare context, as a representative developing-country setting. Furthermore, the article synthesized evidence from contemporary AI-driven appendicitis studies and systematic reviews, and contextualized these findings against Nigeria’s healthcare infrastructure, historical surgical innovations, and prevailing socio-economic constraints. To date, there is no documented clinical deployment of AI for appendicitis management in Nigeria. Major barriers include limited healthcare financing, unreliable power and digital infrastructure, shortages of AI-literate clinical and technical personnel, fragmented health data systems, and the absence of a coordinated national strategy for clinical AI adoption. Nevertheless, Nigeria’s history of pioneering complex surgical procedures under resource-constrained conditions highlights an underlying capacity for technological adaptation. While systemic challenges currently limit near-term implementation, AI-assisted appendicitis management remains a realistic long-term objective. Progress will depend on phased, context-appropriate adoption strategies emphasizing clinical- and laboratory-based models, targeted infrastructure investment, workforce development, and sustained public–private partnerships, drawing lessons from Nigeria’s own trajectory of surgical innovation.
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Copyright (c) 2026 Opeyemi Q. Asafa, Aishat O. Asafa, Alao K. Ayodele, Emmanuel O. Folami, Kehinde Awodele, Kehinde A. Alatishe, Adejare Adedire, Ganiyu A. Oyeniyi, Uthman Ismail, Olumuyiwa Ajayeoba, Gbenro S. Akintola

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
