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AI-Driven Patient Screening for Clinical Trials in Pancreatic Cancer: The PANCR-AI Pilot Retrospective Comparative
Arthur Claessens1,2, Alizée Simon2, Agathe Manchart2
1Université de Lorraine, Nancy, France.
JMIR Cancer
|February 23, 2026
Summary
Artificial intelligence (AI) significantly speeds up clinical trial screening for pancreatic cancer patients, showing high sensitivity and reducing assessment time from hours to minutes. This AI-driven approach promises to improve efficiency and reproducibility in trial matching.
Area of Science:
- Oncology
- Medical Informatics
- Artificial Intelligence in Healthcare
Background:
- Clinical trial screening is a bottleneck in cancer research, particularly for pancreatic cancer due to its poor prognosis.
- Artificial intelligence (AI) presents a potential solution to enhance the speed and accuracy of clinical trial matching.
- The PANCR-AI pilot study focuses on evaluating AI's utility in screening for clinical trials in advanced pancreatic cancer.
Purpose of the Study:
- To evaluate the performance of AI models in assessing clinical trial eligibility criteria.
- To compare AI-based screening against a double-blind human expert consensus (gold standard).
- To analyze factors influencing screening success and time efficiency.
Main Methods:
- Retrospective analysis of advanced pancreatic cancer patient cases (2018-2023) screened against available clinical trials.
- Comparison of three large language models (GPT-4.5, Claude 3.7 Sonnet, Mistral-7B-Instruct v0.3) against a gold standard established by two blinded oncologists.
- Evaluation using standard metrics (sensitivity, specificity, precision, recall, F1-score) and analysis of screening time and criterion complexity.
Main Results:
- AI models achieved high sensitivity (83.3%-92.2%) in identifying potential trial eligibility across 341 patient-trial pairs.
- Screening time was drastically reduced with AI (2.53-3.15 hours) compared to manual assessment (44.70 hours).
- A correlation was found between criterion length (words/characters) and the risk of screening failure; more open trials correlated with higher patient inclusion likelihood.
Conclusions:
- AI demonstrates significant promise for improving the efficiency and accuracy of clinical trial screening in oncology.
- Future research should focus on integrating AI with structured clinical data and prospective validation for real-world application.
- Successful implementation requires addressing challenges in multimodal comprehension and electronic health record integration for scalable clinical use.
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