Introduction
Systematic reviews in precision oncology rely on screening thousands of articles to identify randomized controlled trials that evaluate targeted therapies, biomarkers, and patient outcomes. This manual annotation process is labor-intensive, time-consuming, and susceptible to variability across reviewers. Large language models (LLMs) offer rapid classification and data extraction, but their reliability can be uneven without domain-specific prompts and safeguards. A human-LLM collaborative annotation approach seeks to combine the speed of LLMs with the expertise of clinicians and methodologists to improve both efficiency and accuracy in screening precision oncology randomized controlled trials.
A Concept for Human-LLM Collaboration
The central idea is to implement a hybrid workflow where LLMs perform initial triage and data capture, followed by careful human adjudication. This arrangement leverages LLMs for high-volume tasks while enabling domain experts to verify inclusion criteria, assess risk of bias, and resolve ambiguities. The ultimate goal is to produce high-quality include/exclude decisions, transparent rationales, and well-structured data extractions that feed downstream meta-analyses.
Proposed Workflow
Stage 1: LLM-Assisted Pre-Screening
Articles are first screened by an LLM using prompts tailored to precision oncology. The model assigns a classification label such as include, exclude, or unclear, along with a confidence score and a concise rationale. Key PICO elements, trial design, and primary outcomes are extracted when feasible. This stage dramatically reduces the number of articles sent to human reviewers.
Stage 2: Human Validation
Clinical and methodological experts review LLM-flagged items, confirming or overturning the model’s decisions and refining the extracted data. Reviewers document the rationale for every decision, creating a transparent audit trail that supports reproducibility and auditability.
Stage 3: Active Learning and Error Analysis
Uncertain cases with low confidence are prioritized for review and used to fine-tune prompts and the model’s internal representations. Periodic error analysis identifies systematic biases or gaps in coverage, guiding targeted updates to the annotation schema and training data.
Stage 4: Evidence Synthesis and Reporting
Validated studies are integrated into the data extraction sheet and appraised for risk of bias and applicability. The results feed into the PRISMA flow diagram, with clear documentation of decisions and confidence in the included studies. This structure supports transparent reporting and scalable updates as new trials emerge.
Quality Assurance and Metrics
A robust evaluation framework is essential for trust in a human-LLM annotation pipeline. Key metrics include recall of include trials, precision of include decisions, and F1 scores for screening. Inter-annotator agreement (for human reviews) and model-calibrated confidence thresholds help quantify reliability. Ongoing monitoring should assess bias, coverage of subgroups (targets, biomarkers, cancer types), and the consistency of data extraction across reviewers.
Practical Considerations
Implementation requires careful prompt design, version control, and integration with systematic-review platforms. Prompts should be modular, allowing rapid updates as guidelines evolve. Data governance, privacy, and auditability are essential when handling trial reports and patient-level outcomes. Open workflows that track prompts, outputs, and human adjudications enhance transparency and reproducibility.
Expected Impact and Next Steps
A well-implemented human-LLM collaboration can dramatically reduce screening time while preserving, or even improving, accuracy in identifying relevant precision oncology RCTs. This approach supports more timely evidence synthesis, enhances consistency across reviews, and provides a scalable framework for future updates as new trials become available. Next steps include pilot studies on curated datasets, cross-institutional validation, and the development of standardized annotation schemas that capture trial-level nuance.
Challenges and Ethics
Risks include overreliance on automated decisions, hallucinations in AI outputs, and potential bias in model prompts. Transparent reporting of model confidence, explicit human oversight, and detailed rationale notes are essential. Researchers should ensure that the workflow remains auditable, respects patient privacy, and adheres to established methodological standards for systematic reviews.
Conclusion
Integrating human expertise with LLM capabilities offers a pragmatic path to accelerate screening of precision oncology randomized controlled trials. By combining fast, scalable AI-assisted triage with rigorous human validation, this collaborative annotation approach aims to improve accuracy, reproducibility, and efficiency in evidence synthesis.
