Proceedings of Botho University International Research Conference (BUIRC 2025)

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Artificial Intelligence-Driven Healthcare Diagnosis in SADC: Developing A Lesotho-Specific Disease Detection Model

Authors
Wole Michael Olatokun1, *, Mathabo Mamello Rampanyane2
1National University of Lesotho, Roma, Lesotho
2Botho University, Maseru, Lesotho
*Corresponding author. Email: wm.olatokun@nul.ls
Corresponding Author
Wole Michael Olatokun
Available Online 12 December 2025.
DOI
10.2991/978-94-6463-906-3_29How to use a DOI?
Keywords
Artificial Intelligence-Driven Healthcare; AI-driven diagnostic tools; Disease Detection Model
Abstract

The integration of Artificial Intelligence (AI) in healthcare offers transformative potential, especially for resource-limited settings like Lesotho, a mountainous country in the Southern African Development Community (SADC) region. Lesotho faces significant challenges including high burdens of tuberculosis (TB), HIV/AIDS, and non-communicable diseases, compounded by limited access to medical facilities and a shortage of healthcare professionals. This paper explores the development of an AI-driven diagnostic model tailored specifically to Lesotho’s healthcare context, aiming to enhance early and accurate disease detection. Through systematic document analysis, the study evaluates existing AI diagnostic tools and their applicability to Lesotho’s health challenges. Preliminary findings show that AI-based diagnostics improve detection rates with high sensitivity and specificity, enabling timely interventions and alleviating strain on the healthcare system. The model emphasizes cultural and contextual relevance, addressing diseases prevalent in Lesotho while leveraging machine learning and neural networks to analyze complex medical data. By providing a comprehensive examination of AI’s role in disease detection within Lesotho, this paper offers valuable insights for healthcare practitioners and policymakers seeking to harness AI innovations to improve health outcomes in similar resource-constrained environments. It does not any methodology or framework but based on preliminary results.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of Botho University International Research Conference (BUIRC 2025)
Series
Atlantis Highlights in Sustainable Development
Publication Date
12 December 2025
ISBN
978-94-6463-906-3
ISSN
3005-155X
DOI
10.2991/978-94-6463-906-3_29How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Wole Michael Olatokun
AU  - Mathabo Mamello Rampanyane
PY  - 2025
DA  - 2025/12/12
TI  - Artificial Intelligence-Driven Healthcare Diagnosis in SADC: Developing A Lesotho-Specific Disease Detection Model
BT  - Proceedings of Botho University International Research Conference (BUIRC 2025)
PB  - Atlantis Press
SP  - 458
EP  - 470
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6463-906-3_29
DO  - 10.2991/978-94-6463-906-3_29
ID  - Olatokun2025
ER  -