Iterative Development of an AI-Assisted Data Extraction Tool for Literature Synthesis in Oncology.

Authors

Anne Liu, Faisal Alfadli, Philip Wong

Published in

Cureus. Volume 18. Issue 8. Pages e114999. Epub Aug 22, 2026.

Abstract

As oncology research continues to advance, the synthesis of clinical trial evidence is becoming more demanding for healthcare professionals, yet it remains critical for improving the provision of care. While sometimes inconsistent, large language models (LLMs) have emerged as a potential solution to automate literature screening and data extraction. This technical report describes the iterative development of an AI-assisted literature synthesis tool for radiation oncology research. A screening and data extraction tool was developed in three phases using ChatGPT-4o. In Phase 1, prompts were iteratively refined to screen 840 abstracts for Phase 2/3 radiotherapy trials in small cell lung cancer (SCLC). Screening performance was compared against manual reviewer consensus using sensitivity and specificity. In Phase 2, 13 clinical trial variables were extracted from 18 eligible studies and evaluated against a manual extraction reference with a weighted scoring rubric. In Phase 3, a web-based application was developed using the optimized prompts from the first two phases and pilot-tested on eight healthcare professionals for usability and workflow relevance. In the first two phases, the initial prompts had many false positives and inconsistent extractions. Iterative prompt refinement across the second and third batches of studies improved both screening accuracy and extraction consistency. In the validation batch, the final screening model achieved 100% sensitivity and specificity, and the data extraction prompts obtained a mean score of 12.0 (SD: 0.5) out of 13. During Phase 3, pilot testers reported that the application helped them read and compare clinical trial data through concise, structured tables. Users also provided suggestions for future development, including role-dependent personalization and visualization tools. Prompt-engineered LLM models show potential for improving efficiency and accessibility of literature screening and data extraction in oncology research. Future work will integrate the feedback obtained and externally validate the tool using larger independent datasets and multidisciplinary user cohorts.

PMID:
42774013
Bibliographic data and abstract were imported from PubMed on 23 Sep 2026.

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