aceAI-assisted characterization of psychedelic clinical trials from ClinicalTrials.gov
Abstract Background The registry record of psychedelic and related rapid-acting compounds is large, heterogeneous and laborious to synthesize by hand. We tested whether a large language model (LLM) with live access to the ClinicalTrials.gov application programming interface (API) could produce a timely, structured and reproducible characterization of this landscape. Methods Cross-sectional analysis of interventional trials on ClinicalTrials.gov as of 13 January 2026. The core-set comprised classic serotonin 5-HT…
An LLM connected to the registry API, using documented field-specific query expressions, produced a structured and reproducible map of 412 core-set psychedelic trials plus 300 ketamine/esketamine trials.
Open-ended conversational querying by the LLM underestimated the registry record several-fold for some compounds, so unstructured use gave unreliable counts.
The authors state the approach complements rather than replaces systematic review, and its accuracy against manual extraction remains to be quantified.
Evidence
- Peer-reviewedTrials2026-10-14
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Truvace Impact Record TRV-2026-1435, v1: “AI-assisted characterization of psychedelic clinical trials from ClinicalTrials.gov.” Truvace, 2026-10-15. /record/TRV-2026-1435 (accessed at citation time). sha256 c0d01eb1f0ca12bf…
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