Transformation Concept of Artificial Intelligencein the Early Identification of Tuberculosis Recovery Phase: A Systematic Literature Review

Abstract

Background: Another chronic problem in tuberculosis (TB) eradication programs amid the widespread use of artificial intelligence (AI) in the health world is that early identification of the healing phase has not been found using similar technology. The purpose is to find if the transformation concept of AI will be able to help identify symptoms of early recovery of TB. Methods: This research used a document review with a descriptive design. Data was processed by PRISMA analysis. The keywords were Artificial Intelligence, convalescence phase, and tuberculosis. The data were filtered from Google Engine included from Google Scholar, ResearchGate, PubMed, and Semantic Scholar screened in the last 5 years (2018-2023), in English or Indonesian. The stages of document screening were adjusted to the PRISMA diagram, and were analyzed descriptively. Results: The study shows that the majority of AI studies discussed diagnosis (n=9 or 69.2%), only 3 documents (23.1%) discussed on TB treatment, and 1 document (7.7%) on monitoring. In conclusion, early identification of the recovery phase of TB patients is supported by previous researchers and can be done in the form of an application. Conclusions: Artificial intelligence in the TB eradication program has value especially if conducted integrated with other health programs.

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