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 to transform endpoint assessment in clinical trials.
Why Was AI Needed?
- Histological assessment is often subjective.
- Significant interobserver variability exists among expert pathologists.
- Manual review is labour-intensive and time-consuming.
- Inconsistent interpretation may affect trial outcomes and regulatory decisions.
- Standardised and reproducible assessment has long been a major unmet need.
What Has Changed?
- In 2025, the FDA and EMA endorsed **AIM-NASH**, the first AI-enabled clinical trial end point.
- AIM-NASH uses deep learning to evaluate liver biopsy histology in MASH clinical trials.
- AI demonstrated high agreement with expert pathologists while improving consistency.
- This represents the first regulatory acceptance of an AI-based efficacy end point.
- The approval establishes a regulatory pathway for future AI-enabled biomarkers.
Potential Clinical Impact
- More objective assessment of treatment response.
- Reduced observer variability.
- Improved patient selection.
- Faster central pathology review.
- Shorter and potentially less expensive clinical trials.
- Better standardisation across international multicenter studies.
- Potential expansion to oncology, gastroenterology, cardiology, radiology, and other specialities.
Take-Home Messages
- AI has moved from decision support to regulatory-accepted clinical trial evaluation.
- AIM-NASH represents a historic milestone in AI-assisted drug development.
- AI is expected to complement—not replace—clinical experts and pathologists.
- Rigorous validation and regulatory oversight remain essential.
- This approval may fundamentally reshape the design and conduct of future clinical trials.