The Use of AI Expands Its Role in Healthcare, from Early Detection of Dengue Fever to Pandemic Preparedness

The application of artificial intelligence (AI) technology is becoming increasingly widespread across various sectors, including healthcare. The role of AI is no longer limited to chatbots or information search engines; it has expanded into areas such as clinical data processing, decision-making support, and the early detection of potential disease outbreaks. Try AI Tools

This was revealed by Anis Fuad, S.Ked., DEA., a researcher at the Center for Tropical Medicine (PKT) at Universitas Gadjah Mada (UGM), during the *TropmedTalk* podcast, which aired on Wednesday (October 7).

ExplainDengue Innovation and Outbreak Mitigation

One of the flagship research initiatives currently being developed by PKT UGM is "AI for Pandemic Preparedness" (AI4PEP). This project is part of a global research effort focused on leveraging artificial intelligence (AI) to strengthen preparedness against future pandemic threats.

As part of this research, the team has developed a platform called *ExplainDengue*. The platform integrates historical data on dengue fever cases from healthcare facilities with variables related to climate, weather, and rainfall. Its objective is to predict the dynamics of dengue risk and map out areas potentially prone to a surge in cases.

Anis explained that an uncontrolled rise in dengue cases risks triggering an outbreak—or what is termed in Indonesia as an "extraordinary occurrence" (*kejadian luar biasa* or KLB)—which could overwhelm healthcare facilities.

"If the potential for an increase in cases can be identified early, the hope is that such impacts can be avoided," said Anis.

Beyond dengue, the research team is also exploring the use of AI to detect potential outbreaks by analyzing electronic medical records (EMR) in emergency departments.

By leveraging large language models (LLMs), data on patients' chief complaints and medical histories are parsed to identify patterns of specific syndromes that could serve as early warning signs of an outbreak, allowing health authorities to heighten their vigilance at an early stage.

Data Quality Challenges and the Risk of “Pilotitis”

Despite its immense potential, the development of AI in the healthcare sector faces significant challenges. Anis points out that the reliability of an AI system depends heavily on the availability of data that is accurate, complete, consistent, and kept up to date.

Beyond data-related issues, the sustainability of innovation is a critical concern. Technologies that have been developed must be effectively implemented in real-world settings and subjected to regular feedback. Without practical application, there is a risk that innovations may stall at the trial stage—a phenomenon Anis refers to as “pilotitis.”

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