TrialPanorama/Dataset

TrialPanorama: Supervised Fine-Tuning Data for Clinical Research LLMs

Dataset Summary

TrialPanorama SFT is a large-scale, task-oriented supervised fine-tuning (SFT) dataset designed to train large language models for end-to-end clinical research and trial development workflows.

The dataset is derived from TrialPanorama, a structured clinical research resource aggregating 1.6M+ clinical trial records across global registries and linking them with biomedical ontologies and supporting literature. It focuses on transforming raw clinical trial data and curated evidence into instruction–response pairs that reflect realistic, expert-level research tasks.

The dataset supports training LLMs to operate as clinical research assistants capable of systematic literature review, trial design reasoning, and evidence-based decision making.

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Supported Training Tasks

Each task is released as a separate dataset configuration.

Task (config)	Description
study_search	Given a clinical research question, retrieve and justify relevant studies from large trial and literature corpora.
study_screening	Perform inclusion/exclusion decisions for candidate studies based on eligibility criteria and study metadata.
evidence_summarization	Synthesize structured and unstructured trial evidence into concise, faithful summaries.
trial_design	Generate or refine clinical trial designs, including arms, interventions, and eligibility criteria.
sample_size_estimation	Estimate appropriate sample sizes under specified statistical and design assumptions.
trial_completion_assessment	Assess trial completion likelihood and rationalize risks using trial design and historical evidence.

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Data Characteristics

• Instruction-following format suitable for SFT
• Grounded in real clinical trial records
• Emphasizes clinical reasoning, not surface text generation
• Covers both systematic review and trial design & optimization tasks
• Designed to support generalist and agentic LLM training

All files are provided in Apache Parquet format.

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Typical Fields

Each record may include:

• Task-specific instruction or prompt
• Structured context (trial metadata, eligibility criteria, outcomes, phase)
• Model response targets written or validated by domain experts
• Task and difficulty metadata

Exact schemas vary by task.

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Intended Use

This dataset is intended for:

• Supervised fine-tuning of LLMs for clinical research tasks
• Training research-oriented AI agents for trial planning and evidence synthesis
• Building domain-adapted models for systematic review automation
• Academic benchmarking of clinical reasoning capabilities

Not Intended For

• Model evaluation (see TrialPanorama benchmarks for evaluation)
• Clinical decision making
• Direct medical or regulatory use

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How to Load

Load a specific training task via its configuration name:

from datasets import load_dataset

ds = load_dataset("TrialPanorama/dataset", "study_screening")
ds["train"]