Page 20 - GHES-2-3

Global Health Economics and
Sustainability
AI in antibiotic prescribing in Nigeria

5 Department of Microbiology, College of Life Science, Kaduna State University, Kaduna, Nigeria
6 Department of Medical Laboratory Science, Faculty of Allied Health Sciences, Kaduna State University, Kaduna, Nigeria
7 Department of Microbiology, Faculty of Life Sciences, Bayero University Kano, Kano, Nigeria

1. Introduction that AI is a highly effective tool for managing antibiotic
resistance. Gathering clinical data to create clinical
It is widely believed that the persistent and indiscriminate decision support systems (CDSS) could aid clinicians in
use of antibiotics is a primary contributor to the alarming tracking antimicrobial resistance (AMR) trends, thereby
surge in multidrug and extremely drug-resistant pathogens. encouraging the judicious use of antibiotics (Lau et al.,
At present, bacterial resistance to once-effective common 2021; Kaplan et al., 2023; Valderrama-Rios et al., 2023).
antibiotics presents a formidable challenge. Many experts
fear that without intervention, this issue would undermine 2. Historical perspective of applying AI in
the positive strides made in antibiotic discovery and therapy healthcare settings
developments. Coupled with challenges like the exorbitant
costs and limited availability of antibiotics in remote rural During the 1960s, Stanford University researchers
areas, there is a growing impetus to explore sustainable developed the inaugural problem-solving program known
approaches for mitigating bacterial resistance to antibiotics. as “Dendral,” designed to assess hypotheses. Its primary
In response to these concerns, the concept of employing objective was to aid pioneers in organic chemistry by
artificial intelligence (AI) for antibiotic prescribing and identifying unknown samples based on their mass spectra.
clinical support in Nigerian health-care settings has been This pioneering system was later utilized to identify bacteria
formulated. This initiative seeks to address the complex responsible for severe blood infections and recommend
interplay of factors contributing to bacterial resistance suitable antibiotic treatments. In 1984, an early article on
to antibiotics, aiming to establish a more sustainable and AI utilization was published, introducing the computer-
effective framework for antibiotic use in the healthcare assisted medical decision-making system known as
landscape (Jiménez-Luna et al., 2021; Goldberg et al., 2024). SHELP, aimed at diagnosing inborn errors of metabolism
(Fanelli et al., 2020; Sahu et al., 2022). The application of AI
AI is a scientific field focused on the computational
understanding of what is commonly referred to as intelligent in healthcare gained widespread attention in 2016, when
AI software incorporated into the International Business
behavior (Fanelli et al., 2020). It combines many disciplines, Machines (IBM) Watson platform accurately diagnosed
such as data science, computer, and information science, a rare form of leukemia in a 60-year-old woman and
dedicated to crafting systems that mimic human intelligence proposed an effective treatment plan (IBM, 2023).
and execute numerous tasks such as natural language
processing, decision-making, speech recognition and Since 1984, there has been a notable rise in AI-focused
visual perception (Fanelli et al., 2020). AI is rapidly gaining publications in pediatrics. This includes AI applications in
prominence in health-care settings (GAO, 2020; Sarkar emergency management, such as automatic appendicitis
et al., 2023). Current AI algorithms support diagnostic risk stratification, diagnostic decision support, and a
and prognostic assessments in various medical specialties, framework for asthma exacerbation prediction (Christaki,
finding applications in hospitals, and clinical settings 2015; ECDPC, 2021). In the field of pediatric oncology, AI
(GAO, 2020). The potential applications of AI in healthcare contributes to the comparative analysis of genes to aid in the
are expansive, promising to accelerate the discovery of new development of anticancer drugs and the profiling of gene
antimicrobial drugs, enhance diagnostic and treatment expression in pediatric conditions such as neuroblastoma and
precision, and concurrently reduce costs (Ali et al., 2022; lymphoblastic leukaemia. Similarly, in pediatric infectious
Tamma et al., 2023). By inputting relevant medical data, the diseases, various AI approaches are employed, ranging
AI tools could analyze and process the information within from the creation of novel antimicrobial medications to the
a given dataset, generating logical responses that aid in accurate diagnosis and effective management of infectious
patient diagnosis and treatment outcome prediction. ailments (Rawayau et al., 2022; Baker et al., 2022).
Nigeria’s healthcare and research sector is faced with 3. The multiple antibiotic resistance crisis in
many challenges, which continue to hinder effective Nigeria
research and health-care delivery. These problems include
the lack of funding, inadequate health-care staff and The problem of multiple antibiotic resistances is regarded
technical know-hows who are directly involved in disease as a serious global health crisis due to its impact on both the
diagnosis, and absence of health-care centers in many epidemiology and persistence of many diseases, prompting
rural areas. Recent studies (Ali et al., 2022). Have shown the World Health Organization (WHO) to develop an

Volume 2 Issue 3 (2024) 2 https://doi.org/10.36922/ghes.2602