Clinical Trials Protocol Authoring Using LLMs

• Clinical Trials Protocol Authoring using LLMs is an approach leveraging large language models to automate protocol drafting, structure, and review with clear regulatory alignment.
• Structured prompting, chain-of-thought reasoning, and retrieval augmentation enable these models to achieve up to 91% accuracy in screening and protocol generation.
• Automated LLM systems can reduce manual workload by up to 90% while improving transparency and interoperability, though human oversight remains essential.

Clinical Trials Protocol Authoring using LLMs refers to the use of generative and instruction-tuned neural LLMs to automate, accelerate, and improve the design, drafting, validation, and review of clinical trial protocols and associated documents. Protocol authoring encompasses activities such as eligibility criteria extraction, endpoint specification, paper design reporting, regulatory alignment, and table/figure generation. Recent advances demonstrate that LLMs, when applied with structured prompting, neural adaptation, retrieval augmentation, and reasoning chains, can deliver significant efficiency gains, improved transparency, and increased scalability for protocol authoring. However, these systems require careful consideration of limitations in clinical reasoning, hallucination risk, human-in-the-loop oversight, and integration with regulatory data standards.

1. Prompting Strategies and Model Architectures

Modern LLM-based protocol authoring leverages multi-step structured prompting and model adaptation to maximize both controllability and interpretability:

• One-shot prompting: The LLM receives a worked example, guiding it to mimic the expected output format for eligibility criteria or protocol text (Hamer et al., 2023, Wang et al., 2023).
• Selection-inference prompting: The model first determines which protocol items are screenable given the patient/trial data, isolating actionable elements (Hamer et al., 2023).
• Chain-of-thought reasoning: The model is constrained to articulate stepwise logic—justifying why criteria are met, not met, or indeterminate. This process makes the inferential basis auditable and transparent, supporting expert review (Hamer et al., 2023, Wang et al., 2023, Jin et al., 2023).
• Neural + discrete hybrid prompting: Some architectures (as in AutoTrial) combine human-crafted tokens (marking section boundaries and instruction types) with continuous, trainable neural prompts that allow seamless extension to new prompt types without retraining the entire model (Wang et al., 2023).

Algorithmically, the process for eligibility classification is often formalized as:

$$\text{For each criterion } c \in \text{criteria:} \begin{align*} &\text{Screenable? } \to \text{Yes/No/Unknown} \ &\text{If Yes:} \ &\quad \text{Chain-of-thought reasoning} \ &\quad \text{Map reasoning} \to \{\text{met}, \text{not met}, \text{unknown}\} \end{align*}$$

AutoTrial generalizes protocol generation by training with the objective:

$y = f(x_s, x_e, x_r, h_p)$

where $x_s$ denotes trial setups, $x_e$ retrieval-based exemplars, $x_r$ discrete instruction, and $h_p$ the neural prompt (Wang et al., 2023).

2. Performance Metrics and Evaluation

LLM protocol generation is assessed at multiple levels:

• Criterion-level screenability and accuracy: E.g., 72% correct screenability and 72% classification accuracy on eligible criteria for the InstructGPT-based model (Hamer et al., 2023).
• Trial-level precision/recall: Combining LLM reasoning with human review raises protocol-level recall to 1.0 and yields precision of 0.71 in identifying trial eligibility, reducing manual review workload by 90% (Hamer et al., 2023).
• Comparative metrics: BLEU, ROUGE, METEOR, CIDEr, Jaccard, and explicit human scoring (winning rates)—with AutoTrial achieving precision of 0.91, recall of 0.92, F1 of 0.91, Jaccard of 0.84 (Wang et al., 2023).
• Explainability: Manual review confirms 87.3% accuracy and 87.8% explanation correctness for criterion-level predictions in TrialGPT (Jin et al., 2023).

A summary of notable metrics across recent LLM-powered authoring tasks:

Model	Criterion-level Accuracy	Trial-level Precision	Workload Reduction
InstructGPT (Hamer et al., 2023)	72%	0.71	90%
AutoTrial (Wang et al., 2023)	91% (F1)	91% (F1)	N/A
TrialGPT (Jin et al., 2023)	87.3%	NDCG@10=0.73	42.6% screeningΔ

These results demonstrate parity or superiority to traditional and expert baselines when models are coupled with structured reasoning and physician oversight.

3. Retrieval-Augmented Generation (RAG) and Knowledge Integration

Advanced authoring pipelines incorporate external clinical knowledge via RAG frameworks (Markey et al., 26 Feb 2024):

• Retrieval module: Contextualizes the prompt by extracting relevant FDA guidance, historical protocols, and structured clinical registry entries.
• Augmented prompting: The retrieved snippets are injected directly into GPT-based models to anchor output to up-to-date standards and minimize hallucination.
• Decision agent: Orchestrates document section-specific context retrieval, with components such as indexers, embedding models, and vector databases.

Empirical findings indicate that RAG augmentation doubles logical compliance scores (from ~40% to 80%) and robustly delivers valid references alongside high terminology accuracy and relevance (Markey et al., 26 Feb 2024). A plausible implication is that RAG pipelines will be required for high-stakes regulatory documentation where sourcing and adherence must be explicit.

4. Protocol Structuring, Ontology Engineering, and Interoperability

Protocol authoring with LLMs can be enhanced by formalizing and encoding protocol elements as structured data and ontologies:

• Structured attribute sets: Eligibility criteria, demographics, and diagnostics are extracted as sets, enabling set-guided and deontic reasoning for controlled document generation, filtering, and ranking (Jullien et al., 19 Sep 2024).
• Ontology generation and merging: LLMs produce OWL-formatted ontologies from trial endpoints and biomarkers, often in a chained-prompt multi-step pipeline for efficiency. Cost per trial ranges from $0.005–0.094 and merging is achieved in O(n) time with O(log n) synonym deduplication (Çakır, 18 Dec 2024).
• FHIR/mCODE interoperability: LLMs process unstructured notes into interoperable, standardized FHIR/mCODE profiles for oncology trials. This approach archives a 92% accuracy rate for complete profiles and exceeds SNOMED, LOINC, and RxNorm mapping accuracy of previous baselines by over 10 percentage points (Shekhar et al., 18 Oct 2024).

These methods allow protocols to be dynamically updated and harmonized for regulatory review, patient matching, and data sharing at scale.

5. Automation, Simulation, and Adaptive Trial Design

LLMs are not only augmenting text drafting but also enabling simulation and adaptive design:

• Bayesian adaptive clinical trials: Fine-tuned LLMs (BACTA-GPT) can translate free-form specifications from trialists into Bayesian hierarchical models and fully executable R/JAGS code. The engine incrementally builds the statistical specification via chain-of-thought workflows. Key model equations include:

$Y[i] \sim \text{Normal}(\mu[i], \sigma^2) \ \mu[i] = \beta_0 + \beta_1 X[i] + \alpha^{A[i]}$

with code generation mimicking the mathematical model (Padmanabhan et al., 2 Jul 2025).

• Synthetic trial data generation: LLMs synthesize complete trial reports for outcome prediction and augmentation via retrieval-reasoning chains that preserve intervention-outcome correspondence; this enables advanced simulation, supports robust classifier training, and adheres to privacy constraints (Xu et al., 16 Oct 2024).
• Automatic evaluation with pseudocode: Frameworks such as BioPlanner and ProtoMed-LLM recast protocols as sequences of admissible pseudofunctions, permitting objective, rapid evaluation using accuracy, sequence consistency, and coverage metrics (O'Donoghue et al., 2023, Yi et al., 6 Oct 2024).

This automation fosters rapid iteration and validation of protocol architectures, reducing both time and technical barrier for advanced design.

6. Human Oversight, Ethical Safeguards, and Limitations

Despite progress, LLM protocol authoring retains persistent limitations:

• Hallucinations and overconfident reasoning: All cited studies highlight instances where LLMs produce plausible but incorrect clinical logic, underscoring the need for expert review—particularly for dropout criteria that may trigger false protocol exclusion (Hamer et al., 2023, Gao et al., 2 Dec 2024).
• Human-in-the-loop necessity: Physician, statistician, or protocol chair review corrects errors—especially for ambiguous cases or when criteria cannot be evaluated from the available data (Hamer et al., 2023, Jin et al., 2023).
• Data and computation constraints: Small, open-source models (Qwen2-7B, Phi3-medium-4k) achieve rapid response (<10s/query) and can be run locally (on GPUs with <30GB RAM) for privacy assurance (Peikos et al., 31 Oct 2024).
• Regulatory compliance/standardization: Structured outputs, schema validation, and robust ontology engineering must be used to ensure protocol drafts are reproducible and regulation-ready (Çakır, 18 Dec 2024, Shekhar et al., 18 Oct 2024).

Precise chain-of-thought explanation, coupled with schema-constrained outputs and expert audit, remains fundamental for clinical deployment.

7. Impact and Outlook

LLM-driven protocol authoring achieves substantial advances in efficiency, consistency, and auditability. Key benefits include:

• Reduction in manual review workload by up to 90% for eligibility screening (Hamer et al., 2023).
• High clinical accuracy and interpretability, especially when enriched with explicit reasoning chains (Wang et al., 2023, Jin et al., 2023).
• Scalable integration of real-world data, ontologies, and knowledge bases for interoperable protocol generation (Shekhar et al., 18 Oct 2024, Çakır, 18 Dec 2024).
• Support for advanced trial designs (adaptive Bayesian models), simulation, and tabular/figure generation for reporting (Padmanabhan et al., 2 Jul 2025, Yang et al., 18 Sep 2024).
• Practical frameworks for human-in-the-loop oversight, privacy protection, and regulatory alignment (Peikos et al., 31 Oct 2024, Gao et al., 2 Dec 2024).

Current limitations—clinical reasoning errors, reliance on high-quality data, and oversights in complex legal/ethical requirements—necessitate further research in hybrid architectures, RAG pipelines, and expanded expert review. The synthesis of structured prompting, retrieval-augmented knowledge, and transparent reasoning with active oversight is poised to define the next generation of scalable, trustworthy clinical trial protocol authoring systems.