ArticleCureus2026
Iterative Development of an AI-Assisted Data Extraction Tool for Literature Synthesis in Oncology.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.