STUDY AUTHORS

Seth Wallack, Andrew Fox, Eric Goldman, Aziz Beguliev, Sommer Aweidah

STUDY PRESENTATION

This study abstract was presented as a poster at the Symposium on Artificial Intelligence in Veterinary Medicine
(SAVY 2.0) in Ithaca, NY | May 16-18, 2025

Click image to view the full poster

Background

In human radiology, several metrics are utilized to assess report usefulness, including diagnostic accuracy and Average Time Reporting (ATR). Studies from 2013 and 2017 found reporting times averaged:

• 98 seconds per thorax image,
• 111 seconds per abdomen image
• or ATR of 85 seconds per report, respectively. ^1,2,3,4

A study for veterinary ATR for radiographs without and with AI assistance could not be found.^5,6

Objectives

• First objective is to baseline ATR for single region veterinary radiograph reporting WITHOUT AI assistance.
• Second objective is to baseline and compare ATR for single region veterinary radiograph reporting WITH AI assistance.

Methods

Case Time Period: January 2023 and April 2025 (28 months).

Case Types: Single region reports were grouped into thorax vs. abdomen and further divided into non-STAT vs. STAT. No distinction was made between typing vs. dictating reports or between canines and felines.

Radiologists: Two US board-certified veterinary radiologists (Drs. Seth Wallack, DACVR and Andrew Fox, DACVR) with between 10-and 20-years experience.

Exclusion Criteria: For the reporting with and without AI assistance, exclusion criteria was cases with ATRs <20 seconds or > 20 minutes.

Platform: Both Radiologists used the patented Vetology® reporting platform featuring personalized generative AI conclusions and recommendations derived from the radiologists’ findings.

AI Generation Time: Separately, average AI generation time was calculated for thoracic and abdominal conclusions and recommendations. Two different findings examples (~200 words and ~300 words) were tested for both thoracic and abdominal AI generation to assess the impact of the findings’ word count on generation time. ATR includes all aspects of report creation from claiming to finalizing each single region case.

WITH AI Assistance:

• • 2489 cases.
  • 2280 used for ATR.
  • 209 cases excluded (8%) for > 20 minutes.

Conclusions

These results reflect a specific veterinary teleradiology company’s baseline ATR with and without AI prelim reporting, recorded at the given points in time. The performance of the platform itself will continue to improve as AI evolves and processing speeds accelerate.

Using the approach described above, this study provides an initial benchmark for veterinary radiology reporting times and suggests that hybrid AI tools may help improve efficiency.

This is a specific veterinary teleradiology company’s baseline ATR with and without AI prelim reporting. This baseline ATR may be comparable to industry-wide ATR for US and European board-certified veterinary radiologists.

This study demonstrates that veterinary radiograph ATR can be reduced by 24% (range 11-33%) using a personalized hybrid generative and visual AI reporting solution. Vetology AI assistance reporting was shown to increase the number of reports per hour from 6.5 to 8.0.

A hybrid model of radiologist-reported findings and Vetology AI-generated, personalized conclusions and recommendations could reduce a 40-case workday by up to 3 hours.

AI-assisted reporting has the potential to reduce radiologist workload fatigue. Generating personalized AI conclusions and recommendations takes seconds.

Funding Disclosures: Vetology Innovations provided funding for this study through salaries.