Introduction Accurate assessment of disease activity in inflammatory bowel disease (IBD) clinical trials relies on central reading of endoscopic and histologic images. Although considered the current gold standard, this process is limited by interobserver variability, operational delays, high costs, and scalability challenges.
Introduction
Accurate assessment of disease activity in inflammatory bowel disease (IBD) clinical trials relies on central reading of endoscopic and histologic images. Although considered the current gold standard, this process is limited by interobserver variability, operational delays, high costs, and scalability challenges. With rapid advances in artificial intelligence (AI) and machine learning (ML), automated image analysis has emerged as a promising tool to improve the accuracy, consistency, and efficiency of endpoint assessment in clinical trials.
To address this evolving field, the International Organisation for the Study of IBD (IOIBD) developed an evidence-based consensus on the appropriate use of AI-assisted evaluation of endoscopic and histologic endpoints in IBD trials.
Summary
This consensus initiative involved a literature review (2018–2025) on AI applications in IBD endoscopy and histopathology. Based on the available evidence, the steering committee formulated 36 statements, which were voted on by 72 IOIBD experts, with ≥80% agreement required for consensus. A total of 45 experts completed the survey, and consensus was achieved for 28 statements.
The panel concluded that AI-assisted central reading could significantly enhance diagnostic accuracy, improve reproducibility, reduce costs, and accelerate image interpretation in IBD trials. Importantly, the consensus strongly favoured a hybrid model combining human expert review with AI support, rather than complete replacement of human interpretation.
However, several challenges remain. These include limited external validation of AI models, concerns regarding generalizability across populations and endoscopy platforms, and dependence on high-quality human-annotated training datasets.
Conclusion
The IOIBD consensus supports the integration of AI/ML tools into endoscopic and histologic assessment in IBD clinical trials, emphasising AI-augmented human decision-making. Future efforts should focus on validation, regulatory guidance, and multimodal data integration, which may ultimately enable broader use of AI in both clinical research and routine IBD care.
Introduction: Artificial intelligence is rapidly transforming healthcare through drug discovery, clinical decision support, remote monitoring, and administrative automation. While AI is widely expected to reduce healthcare costs, this perspective argues that current payment models and...
Introduction: Large frontier AI models such as GPT-5 and Gemini have achieved impressive results across numerous healthcare benchmarks. However, high benchmark scores alone may not reflect real-world clinical reliability. This study systematically evaluated the robustness...
Introduction: Artificial intelligence is rapidly transforming medicine, but its influence extends far beyond healthcare. This JAMA AI Conversations article explores how AI ethics has become a global societal issue, engaging technology leaders, policymakers, healthcare professionals,...
Introduction: Artificial intelligence is rapidly transforming oncology, evolving from image interpretation and pathology analysis to supporting complex clinical decision-making. This perspective argues that AI should enhance the capabilities of oncologists rather than replace their expertise....
Introduction Clinical trial endpoints have traditionally relied on expert human interpretation, particularly for pathology-based outcomes. However, variability between observers, cost, and time remain important limitations. This NEJM AI perspective discusses how artificial intelligence is beginning...
Introduction: As artificial intelligence becomes increasingly integrated into clinical practice, an important question arises: should AI assistants be regulated as medical devices or viewed as evidence-based clinical methodologies? This NEJM AI perspective proposes a new...
We are pioneers in clinical intelligence, dedicated to helping gastroenterologists harness the power of artificial intelligence to drive precision, efficiency, and patient growth.