Perception of Health Care Workers on Artificial Intelligence Based Malaria Diagnosis in Southwestern Nigeria.
Year: 2024
Type: article
Study design: Observational Study
Abstract: Background: Effective control of malaria is anchored on accurate diagnosis. Conventional Methods of diagnosis include microscopy, and malaria rapid diagnosis. Many factors, particularly human error, diagnostic inaccuracies of microscopy due to human errors. The study reports the results of an online survey designed to assess the perception of health workers on artificial intelligence methods in the diagnosis of malaria. Methodology: An online, cross-sectional survey, conducted in April to August 2022. The study was conducted using Google forms. The knowledge of conventional methods of malaria diagnosis and willingness to accept artificial intelligence-based automated malaria diagnosis and parasite counts were assessed. The form (questionnaire) was delivered to emails and several WhatsApp groups. Results: Sixty seven responses were received over the study period, comprising medical doctors (30, 44.8%), medical laboratory scientists (18, 26.9%), postgraduate students (8, 11.9%), nurses (7, 10.4%), and students (4, 6.0%). All the respondents knew about conventional methods of malaria diagnosis. Majority of the respondents (41/67, 61.2%) reported that light microscopy was the most commonly used conventional method of malaria diagnosis. All the respondents reported that they were unaware of artificial intelligence-based malaria diagnosis. The respondents affirmed that artificial intelligence based malaria diagnosis will be a better alternative to the conventional methods and will improve the accuracy of malaria diagnosis. Conclusion: None of the respondents had knowledge of artificial intelligence-based malaria diagnosis; however, respondents affirmed that artificial intelligence-based malaria diagnosis will be a better alternative to conventional methods of malaria diagnosis.
Source: PubMed
Institutions: University of Ibadan, Rice University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4410510833