New AI-Enabled Technology Supports Radiology Follow-Up Throughout Baptist Health - Report - MDSpire
Coming Soon: Introducing MDSpire News. Learn more
Conexiant’s news site is now MDSpire News. Learn more

New AI-Enabled Technology Supports Radiology Follow-Up Throughout Baptist Health

Baptist Health Herbert Wertheim Cancer Institute has launched Eon, an AI-enabled software platform that analyzes radiology reports and helps identify and prioritize incidental findings for clinical review, in an effort to support timely, coordinated patient care.

  • By

  • July 1, 2026

  • 2 min

Share

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

Original Source(s)

New AI-Enabled Technology Supports Radiology Follow-Up Throughout Baptist Health

  • by

  • July 1, 2026

Related Content

Ipsilateral reference may reduce THA fluoroscopy

Operative-time–adjusted findings favored the ipsilateral technique, while absolute fluoroscopy time and radiation exposure remained similar between approaches.

Radiologists may misidentify AI images

CT and MRI images were harder to distinguish than radiographs and ultrasound, while subspecialty expertise was associated with greater accuracy.

Multi-Agent collaboration as a complementary architecture for AI-generated medical examination items