PHILADELPHIA -- A conversational AI workflow could help improve extended focused assessment with sonography in trauma (eFAST) exams, suggest findings presented May 29 at the American Institute of Ultrasound in Medicine (AIUM) annual meeting.
Emergency sonographers showed improved performance with assistance from ChatGPT-5, comparable to conventional instructor-based training, according to a pilot study presented by Nardine Francis from the California University of Science and Medicine in Colton.
“We found that AI is a feasible operational tool that can be used to help teach eFAST exams in medical education,” Francis told AuntMinnie.
eFAST exams use ultrasound to detect conditions in trauma patients, including intraperitoneal free fluid, pericardial effusion, hemothorax, and pneumothorax. This can be challenging for novice learners since these exams are operator-dependent and time-sensitive.
Nardine Francis from the California University of Science and Medicine presents research at AIUM 2026 showing how ChatGPT could help novice learners in eFAST training.AuntMinnie
Previous research suggests that over three in five U.S. MD schools have ultrasound integration, but only one in five has ultrasound education as a current institutional priority. Faculty bandwidth has been cited as a barrier to ultrasound training, highlighting the need for reproducible coaching.
The Francis team studied whether ChatGPT-5 could effectively assist eFAST practice in novice learners and compared results to those from instructor-based learning.
The pilot study included 16 first-year students at the university who had no prior formal ultrasound training. The students were randomized to either AI-assisted or instructor-assisted practice in a 1:1 ratio. In the AI-assisted group, learners entered typed questions and uploaded still ultrasound images into ChatGPT-5. The chatbot provided feedback limited to probe positioning, scan-window landmarks, image quality, and troubleshooting.
Nardine Francis shares how using conversational AI could address education gaps in eFAST training.
Both AI- and instructor-assisted groups showed improved knowledge and confidence, with both areas being close in scoring.
Comparison between AI-assisted, instructor-assisted eFAST training | |||
Measure | Instructor assistance | AI assistance | P-value |
Baseline knowledge | 30.0% | 32.5% | 0.8 |
Post-training knowledge | 85.0% | 87.5% | 0.7 |
Baseline confidence | 1.54 | 1.40 | 0.4 |
Post-training confidence | 5.25 | 4.98 | 0.2 |
Francis reported the AI workflow being workable, with learners able to interact using typed queries and still images without embedded ultrasound AI algorithms.
Despite the success seen in the study, Francis cautioned that long-term knowledge retention cannot yet be inferred and that the results cannot be generalized to unsupervised clinical use.
She said that future work will focus on AI’s best curricular role, with studies having larger sample sizes among learning cohorts.
“We probably want to see whether AI on its own is good or maybe [serve as] an adjunct with instruction by faculty if it’s even better,” she told AuntMinnie.
For more of AuntMinnie's coverage from AIUM 2026, click here.
![Examples of ultrasound findings and techniques. (A) Images in a 39-year-old male patient with a mass in the left thigh. The mass is heterogeneous on the B-mode US image (compared with the patient in D) and showed increased microvascularity (superb microvascular imaging [SMI]) and shear-wave elastography (SWE) values. Undifferentiated pleomorphic sarcoma was diagnosed at biopsy (with pleomorphic rhabdomyosarcoma in surgical specimen). (B) Images in an 18-year-old male patient with a mass in the left leg. The mass is hypoechoic on the B-mode image, with no other findings suggestive of malignancy. The lesion is in contact with the cortex of the tibia, which is slightly irregular. CT revealed a doubtful anteromedial tibial erosion. The microvascular study demonstrated high vascularization, suggestive of malignancy. Periosteal Ewing sarcoma was diagnosed with both histologic and immunohistochemical confirmation. (C) Images in a 69-year-old female patient with a lump growing on the outside of the left leg. Multiple SWE examinations were performed (please note the high values obtained in the measurements, whereas the color map highlights the stiffness relative to adjacent tissues). SMI showed areas of increased vascularization to target for sampling. Undifferentiated spindle cell sarcoma was diagnosed at biopsy, with residual leiomyosarcoma in the surgical specimen after neoadjuvant therapy. (D) Images in a 56-year-old female patient with a mass in the right thigh. The mass is heterogeneous at both B-mode ultrasound (similar to patient A) and MRI (coronal T2-weighted spectral attenuated inversion recovery [SPAIR]; T1-weighted pre-contrast and postcontrast imaging), which even shows uptake after the administration of paramagnetic contrast material, which is traditionally suggestive of malignancy. Low values at SMI and elastography are suggestive of benignity. Spindle cell lipoma was diagnosed at biopsy, with atypical spindle cell lipomatous tumor in the surgical specimen.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/08/images-radiol250278fig2.APCFLSvX6p.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
