Dataset
clinical-trials-ai-2000-2025
An analysis-ready compilation of roughly 3,000 ClinicalTrials.gov studies registered between 2000 and 2025 that involve AI, machine learning, or digital-health tools, enriched with 30 LLM-derived variables covering use case, therapeutic area, sponsor composition, trial phase, deployment score, evidence strength, and responsible-AI keyword indicators.
Data preview
Dataset card
clinical-trials-ai-2000-2025 is a Hugging Face mirror of 3,000 clinical trials involving artificial intelligence, machine learning, or digital-health software, registered on ClinicalTrials.gov and enriched with LLM-extracted analytical variables. It is published by moncefem and distributed in Parquet format under an Apache-2.0 tag.
What this dataset contains
The dataset covers AI/ML/digital-health trials spanning roughly 2000 through 2025, with one tabular row per trial. It pairs identifiers and design attributes drawn from ClinicalTrials.gov with 30 fields produced by per-row LLM extraction, including use-type, disease area, sponsor mix, and a small set of keyword and responsible-AI flags. The single train split contains exactly 3,000 rows.
Structure and fields
Each row carries a synthetic-looking id (large_string), categorical labels such as ai_use_type, disease_area, trial_phase_bucket, and enrollment_bucket, and boolean design and keyword flags including is_completed, is_recruiting, industry_sponsored, has_randomization, has_blinding, mentions_algorithm, mentions_machine_learning, mentions_deep_learning, mentions_ai, mentions_mobile_app, mentions_wearable, mentions_bias, mentions_safety, mentions_privacy, and has_llm. Two float scores summarise maturity and rigor: real_world_deployment_score and evidence_strength_score, while country_count and international_trial capture geographic reach.
How analysts use it
Analysts can slice the corpus by ai_use_type, disease_area, or trial_phase_bucket to track the trajectory of AI in clinical research and benchmark real_world_deployment_score against evidence_strength_score. Sponsor-mix booleans and demographic flags such as includes_children, includes_older_adults, and sex_all support due-diligence style comparisons, while responsible-AI keyword columns (mentions_bias, mentions_safety, mentions_privacy) enable governance screens. For investment work, the file is well suited as a public-records input to theme mapping in healthcare-AI equities and to monitoring sponsor activity over time.
Documentation & references 1
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