Datasets:

TrialPanorama
/

Dataset

license: apache-2.0
language:
  - en
tags:
  - medical
  - biomedicine
  - clinical-trials
  - large-language-models
  - supervised-finetuning
  - evidence-based-medicine
  - trial-design
  - systematic-review
config_names:
  - study_search
  - study_screening
  - evidence_summarization
  - trial_design
  - sample_size_estimation
  - trial_completion_assessment
configs:
  - config_name: study_search
    data_files:
      - split: train
        path: sft_study_search_data_cleaned.parquet
  - config_name: study_screening
    data_files:
      - split: train
        path: sft_study_screening_data.parquet
  - config_name: evidence_summarization
    data_files:
      - split: train
        path: sft_evidence_summarization_data.parquet
  - config_name: trial_design
    data_files:
      - split: train
        path: sft_design_data.parquet
  - config_name: sample_size_estimation
    data_files:
      - split: train
        path: sft_sample_size_data.parquet
  - config_name: trial_completion_assessment
    data_files:
      - split: train
        path: sft_trial_completion_assessment_data.parquet

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.

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.

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.

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.

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

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"]