The Plant Disease Epidemiologist 4.0: The Rise of the Analytical Architect

The practice of plant disease epidemiology has evolved in step with advances in disease measurement, quantitative analysis, and computational modeling. Generative artificial intelligence, particularly emerging agentic systems, introduces a new transition by enabling increasingly complex analytical tasks. We conceptualize the Epidemiologist 4.0 as an analytical architect who designs, supervises, and critically evaluates AI-assisted computational workflows in lieu of manually executing every step. While these responsibilities are not new, genAI expands the scale, speed, and complexity of analyses, placing greater demands on researchers' ability to evaluate outputs. We discuss how genAI is reshaping epidemiological research, from data preparation, statistical modeling, and visualization to literature synthesis, scientific writing, and software development. These capabilities expand analytical possibilities and facilitate the development of reproducible and deployable tools. However, greater analytical capacity also introduces challenges, including over-exploration, questionable analytical choices, unreliable outputs, and difficulties documenting AI-assisted workflows. We argue that delegating computational tasks does not transfer scientific accountability. Epidemiologists must remain responsible for formulating meaningful questions, understanding their data, selecting biologically appropriate methods, validating results, and interpreting evidence. Preparing future epidemiologists requires combining disciplinary and computational foundations with AI literacy, critical verification, and practical experience developing research workflows. Ultimately, the Epidemiologist 4.0 represents not a replacement of traditional expertise but a shift in how it is applied. As computational execution becomes increasingly accessible, the defining contribution of the epidemiologist will lie in scientific judgment and analytical design applied to important questions on the dynamics, prediction, and management of plant disease epidemics.