Clinical Report: New AI-Enabled Technology Supports Radiology Follow-Up

Overview

Baptist Health has launched Eon, an AI-enabled platform designed to analyze radiology reports and prioritize incidental findings for clinical review.

Background

The integration of AI in radiology is becoming increasingly important as it can improve the efficiency of managing incidental findings. The Eon platform addresses challenges in managing follow-up across numerous reports by categorizing findings based on risk and supporting clinical workflows.

Data Highlights

No numerical data or trial data provided in the source material.

Key Findings

• Eon analyzes radiology reports to identify and prioritize incidental findings.
• The platform categorizes findings into low-, medium-, and high-risk categories.
• Low- and medium-risk cases are actively managed through the tool unless the ordering provider opts to manage follow-up directly.
• High-risk findings are flagged for immediate attention through established workflows.
• The technology aims to enhance care coordination and improve clinical workflows.

Clinical Implications

The Eon platform supports timely follow-up care by prioritizing incidental findings, allowing physicians to manage follow-up directly.

Conclusion

The implementation of Eon at Baptist Health focuses on enhancing radiology workflows and patient care.

Related Resources & Content

1. Journal of Medical Internet Research (JMIR), 2026 -- Patients’ Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study
2. conexiant -- Radiology AI in Routine Practice
3. The ASCO Post -- Large AI Breast Cancer Screening Trial Increases Detection Rate by 20%
4. Incidental Findings -- ACR
5. The Role of Artificial Intelligence in Radiology: A Comprehensive Review of Current Workflow Automation, Diagnostic Accuracy, and Future Efficiency Enhancements
6. Incidental Findings
7. https://cs.acr.org/-/media/ACR/Files/Quality-Programs/Measures-Under-Development/JACR-Publication.pdf
8. Large-Scale Evaluation of Machine Learning Models in Identifying Follow-Up Recommendations in Radiology Reports - PubMed