Introduction

Advances in AI have improved lesion detection in upper gastrointestinal (GI) endoscopy. However, most systems still behave as black boxes, providing indings without explanation. In a clinical setting, this lack of interpretability limits adoption. In contrast, vision-language models (VLMs) can provide explanatory outputs, but they lack performance and efficiency. We propose a hybrid system that combines a high-performance lesion detector with a compact VLM to produce structured explanations for upper GI endoscopic images, improving both accuracy and transparency. This system aims to improve classification accuracy for neoplastic vs. non-neoplastic lesions and enhance clinical explainability while enabling effective inference with realistically limited datasets, maintaining clinically practical inference speed during procedures. During the endoscopic procedure, CNN-based detectors can perform at around 10ms per input. Down-stream small VLM can generate 92.8 tokens per second, which is suitable for post-procedure evaluation.

Aims & Methods

We developed a multimodal pipeline integrating a CNN-based lesion detector with a lightweight VLM (Gemma-3-4B). Given an input endoscopic image, the DETR-RT detector predicts lesion type (p0/p1) and bounding boxes, which are passed as metadata inputs to a SigLIP image encoder and a Gemma-3 small language model. Finally, our pipeline outputs description of the context including organ identification, lesion classification, and explanation of the detector’s prediction. We employed a distillation process, in which explanations and descriptions of the input endoscopic image are generated by a very large model such as Gemini-2.5-Pro and GPT-4.1. These descriptions are then used to train a small language model based on Gemma-3 with 4 billion parameters. We prepared 1560 images from 246 clinical cases as experimental data. 1247 images (197 cases) are used for training and the remaining 313 images (49 cases) are used for testing.

Results

On a held-out test set of gastric cases, the system model achieved a 67% sensitivity and 85% specificity of distinguishing neoplastic from non-neoplastic lesions —representing 3% and 2% improvement respectively over the detection model (DETR-RT) alone. Across the held-out test set, structured explanations included consistent fields (organ, lesion type, morphology, rationale) with 100% completeness. Component-wise accuracy: organ (85%), light color (100%), lesion type (75%), and JGES classification (83%).

Conclusion

We developed a compact VLM that generates structured, clinically meaningful explanations for endoscopy images. By fine-tuning with synthetic data and integration of a detector, the model not only identifies abnormalities but explains them based on key morphological and contextual features. Despite limited train data, it achieved high accuracy and interpretability, outperforming detection-only baselines.This approach adds structured explanations without sacrificing performance, enabling efficient training and improving both diagnostic accuracy and transparency. By encoding established endoscopic diagnostic criteria into the output, the system presents clinicians with synthesized clinical knowledge—supporting informed interpretation and enhancing trust in AI-assisted decision making. Notably, the explanations for false positives often referenced benign patterns (e.g., folds, shadows), which are anticipated to support clinicians in interpreting uncertain outputs and assessing potential misjudgments.

References

1. Japanese Gastric Cancer Association. Japanese classification of gastric carcinoma: 3rd English edition. Gastric Cancer. 2011;14(2):101–112.
2. Endoscopic Classification Review Group. Update on the Paris classification of superficial neoplastic lesions in the digestive tract. Endoscopy. 2005;37(6):570–578.
3. Yao K. The endoscopic diagnosis of early gastric cancer. Ann Gastroenterol. 2013;26(1):11–22.
4. Sugimoto M, et al. Risk stratification using the Kyoto classification of gastritis. JGH Open. 2017.
5. Zhang L, et al. AI-EARS: an AI-based endoscopic automatic reporting system improves reporting completeness and diagnostic accuracy. JAMA Netw Open. 2023.
6. Park SH, et al. Vision-language models in gastrointestinal endoscopy: current status and future potential. Dig Endosc. 2023.
7. Mu Y, et al. Development and validation of a deep learning system for gastric inflammation classification based on the Kyoto classification. Gastric Cancer. 2021.
8. Banks M, et al. British Society of Gastroenterology guidelines on the diagnosis and management of patients at risk of gastric adenocarcinoma. Gut. 2019;68(9):1545–1575.
9. Dinis-Ribeiro M, et al. Management of precancerous conditions and lesions in the stomach (MAPS II): ESGE/EHMSG/ESP/SPED guideline update 2019. Endoscopy. 2019;51(4):365–388.
10. OpenAI. GPT-4 Technical Report. 2024. https://openai.com/research/gpt-4
11. Google Research. PaliGemma: Open Vision-Language Models. 2024. https://ai.googleblog.com/2024/03/paligemma.html

Introduction

Artificial intelligence (AI) characterization (CADx) systems for colonic polyps exhibit high diagnostic accuracy. However, they may only support, not replace, clinical decision-making and little is known of this human-AI interaction – even less so with trainee endoscopists. This issue is difficult to explore, but data from a study investigating CADx-supported trainee endoscopists compared to AI-blinded experts could give insight.

Aims & Methods

This analysis draws on data from a prospective observational study comparing the diagnostic performance of CADx-supported trainee endoscopists (using GI Genius®, Medtronic) to expert endoscopists blinded to AI support. Two key scenarios were explored: (a) instances in which the CADx system produced a ‘no prediction’ output, and (b) cases where trainees actively overruled AI-generated diagnoses. These were assessed to examine the impact of AI absence and human-AI discordance on diagnostic accuracy.

Results

The baseline cohort included 11 trainees performing 225 colonoscopies, mean age 63.8 years (SD 12.7), 48.9% male patients. Baseline ADR was 57.8%. Of 630 resected lesions (median size 3 mm, IQR 2;4), 40.0% were in the rectosigmoid, and 53.6% were non-adenomatous. CADx failed to provide a prediction in 19.2% (n=121) of cases. These ‘no prediction’ polyps were smaller (median 3 mm, p=0.048) and more often located in the right colon (72% vs. 58%, p=0.005. In the absence of AI support, trainee diagnostic accuracy declined significantly by 12.8% (63.6% vs. 76.4%, p=0.007). Interestingly, expert endoscopists—despite being blinded to AI—also performed worse on these polyps (64.7% vs. 73.9%, p=0.045), suggesting an inherent difficulty in classifying these lesions.
Trainees overruled AI suggestion leading to discordant diagnoses in 9.0% (57/630) of polyps, comprised of 20 (35.1%) non-adenomatous, and 37 (64.9%) adenomatous lesions. Trainees incorrectly overruled AI in 59.6% (34/57), which mostly consisted of downgrading lesions to NICE 1. Discordant polyps did not differ in size (3 mm vs. 3 mm, p=0.413), but were more often located proximally (77.2% vs. 58.1%, p=0.005). The difference in false negative rate (FNR) between trainees and AI was 62.16% (81.07% vs. 18.92%, p<0.0001), demonstrating a risk of missed adenomas when trainees override AI. Blinded expert endoscopists achieved identical overall diagnostic accuracy in those polyps as the AI system (59.6%). However, their error profiles differed – In this subset, blinded experts and AI performed equally in overall accuracy (59.6%), although with differing error profiles: AI showed a numerically lower FNR (18.9% vs. 35.1%, Δ16.2%, p=0.17), offset by a higher false positive rate (80.0% vs. 50.0%).

Conclusion

CADx systems generate ‘no prediction’ outputs in a substantial proportion of polyps, significantly impairing trainee diagnostic performance. These cases may represent inherently more challenging lesions. Despite CADx’s high accuracy, trainees overrule AI in almost every tenth polyp, most often incorrectly downstaging polyps. Structured training on appropriate use of CADx, emphasizing both its capabilities and limitations, may enhance clinical effectiveness. Further research on human-AI interaction and trust is warranted, as these factors will be critical for successful integration of CADx into routine endoscopic practice.

Disclosure

The original study (AC-CADx) received technical support by Medtronic. Medtronic was not involved in the design of the study or analysis of the data, neither in the original study nor this exploratory analysis.

Introduction

Women have been historically underrepresented in gastrointestinal endoscopy research. Understanding the current patterns of female authorship is essential to assess progress and identify persistent gaps within this field.

Aims & Methods

This study aimed to evaluate the representation of women as first and senior (last) authors the three highest-impact journals in gastrointestinal endoscopy (Endoscopy, Gastrointestinal Endoscopy and Digestive Endoscopy) from 2015 to 2024. For each publication, we extracted bibliometric data of original articles, review articles and editorials, including the names of the first and senior authors, their primary country of affiliation, and the year of publication. Countries and territories were categorized by geographic region. Author gender was predicted using Genderize.io software (https://genderize.io), and only authors with ≥80% gender prediction accuracy were included.

Results

We analyzed 4,414 publications. Among first authors, 885 (20.15%) were women from 36 countries and 3,507 (79.85%) were men from 49 countries. Most female first authors were affiliated with institutions in North America (36.61%), while most male first authors were based in Asia (40.82%). The greatest gender disparity among first authors was observed in Asia (male-to-female ratio = 6.8), and the lowest in Europe (male-to-female ratio = 2.5). Among senior authors, 433 (11.51%) were women from 32 countries and 3,328 (88.49%) were men from 47 countries. Female senior authors were most frequently from Europe (38.57%), whereas male senior authors were again predominantly from Asia (41.12%). The largest disparity in senior authorship was in Oceania (male-to-female ratio = 44), and the lowest in Latin America (male-to-female ratio = 4.1). Notably, the odds of a woman being the first author nearly doubled when the senior author was also a woman. Overall, over the 10-year period, the male-to-female ratio among first authors improved from 4.46 (2015–2016) to 3.25 (2023–2024), and among senior authors from 9.10 to 6.43.

Conclusion

Women remain underrepresented in first and senior authorship positions in gastrointestinal endoscopy research. These disparities may be influenced by limited female representation and mentorship in the field. Although gender gaps have narrowed over the past decade, sustained and coordinated efforts are needed to promote equity in academic publishing.