Comparing Causes of Patient Delay in Tuberculosis Patients According to Four Renowned Artificial Intelligence (AI): Quo Vadis Bibliometric Analysis? Fajar Awalia Yulianto 1,2- Ella Nurlaella Hadi 1- Nurhayati Adnan 1
1. Faculty of Public Health, Universitas Indonesia
2. Faculty of Medicine, Universitas Islam Bandung
Abstract
Patient delay in seeking healthcare is a persistent problem that can lead to delayed diagnosis and suboptimal treatment outcomes. This study was aimed to compare the results of four subscribed AIs (ChatGPT, Jenni, Copilot, and Scopus AI) in identifying the causes of patient delay among tuberculosis patients and their quality in answering the question prompts. The qualitative study was conducted involving the in-depth interviews by the standardized prompts (in Bahasa) with four AI as the informants. The matrix of AI answers was created to analyse the answers of each AI for resemblances or differences. All AI agree that stigma and access to the health service are the cause of the patient delay, followed by socio-economic status that appear in majority AI. ChatGPT provides abundant answers for every prompt with vast language fluency compared to Jenni that gives the briefest and mixed-language answers. Copilot and Jenni give direct address to the reference they give although the first one gives more references compared to the later. Finally, the Scopus AI excelled with concise and expandable answers, suggested deeper prompts, references, concept maps, and emerging themes for the next research topics that we can use. In conclusion, Scopus AI is the most effective AI in this study that can significantly enhance research and streamline bibliometric analysis.
