EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning
arXiv:2601.03471v3 Announce Type: replace Abstract: Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effects at the population level. Existing medical question answering benchmarks primarily emphasize clinical knowledge or patient-level reasoning, yet few systematically evaluate evidence-grounded epidemiological inference. We present EpiQAL, the first diagnostic benchmark for epidemiological question answering across diverse diseases, comprising three subsets built from open-access literature. The three sub
The proliferation of advanced large language models necessitates rigorous and specialized benchmarks to evaluate their real-world applicability in complex domains like epidemiology.
This benchmark offers a crucial tool for assessing and improving AI's capacity for critical reasoning in public health, moving beyond merely 'knowing' facts to 'inferring' actionable insights.
The introduction of EpiQAL shifts the focus of AI evaluation in medicine to include evidence-grounded epidemiological inference, expanding beyond clinical knowledge or patient-level reasoning.
- · Public Health Organizations
- · AI Developers in Healthcare
- · Epidemiologists
- · Global Health Initiatives
- · Obsolete AI Benchmarking Tools
- · AI Models Lacking Reasoning Capabilities
Improved epidemiological forecasting and response through better AI tools.
Increased trust and adoption of AI in public health decision-making.
Potentially reduced impact of future pandemics due to enhanced AI-driven analysis.
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Read at arXiv cs.CL