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Related Experiment Video

Updated: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Expert Knowledge Elicitation: Accessing the Big Data in Experts' Brains.

Jacobo Robledo^1,2,3,4, Aaron I Plex Sulá^1,2,3,4, Lauren G Jaworski^1,2,3,4

¹Plant Pathology Department, University of Florida, Gainesville, FL, U.S.A.

|September 15, 2025

Summary

This summary is machine-generated.

Expert knowledge elicitation makes valuable insights accessible for plant health challenges. This systematic approach synthesizes expert knowledge, informing crucial decisions for global plant health and disease management.

Keywords:

Bayesian update artificial intelligence big data decision support epidemic modeling expert elicitation expert opinion future scenarios knowledge gaps natural language processing prior knowledge

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Area of Science:

Plant Pathology
Agricultural Science
Data Science

Background:

Expert knowledge remains largely inaccessible to digital systems, hindering timely decision-making in plant pathology.
Objective data are often incomplete for addressing urgent, uncertain, or future plant health challenges like emerging diseases.

Purpose of the Study:

To explore the effectiveness of expert knowledge elicitation for plant health challenges.
To emphasize its role in informing expert-based decisions and integrating with big data streams.
To outline future opportunities for scaling expert knowledge elicitation in global plant health.

Main Methods:

Systematic approach to accessing and synthesizing subject matter expert insights.
Framing expert knowledge as big data, integrating with remote sensing, crowdsourced reports, and digital surveillance.
Capturing outputs as scalable datasets (text, tabular, audio, video) for AI-supported synthesis and Bayesian analyses.

Main Results:

Expert knowledge elicitation provides valuable data for addressing complex plant pathology issues and knowledge gaps.
Real-world implementations offer lessons for eliciting, structuring, and interpreting expert-derived data.
Integration with big data and AI enhances inference and transparency in understanding uncertainty.

Conclusions:

Expert knowledge elicitation is crucial for timely, expert-based decisions in plant health, especially with incomplete data.
It can be scaled and strengthened through integration with artificial intelligence and big data streams.
This approach supports global plant health initiatives by making expert insights actionable.