arXiv:2605.29096v1 Announce Type: new Abstract: This paper examines records retrieved from the ClinicalTrials.gov registry to characterize temporal trends in AI terminology and the geographical distribution of AI trials. The work also reports on an exploratory hybrid human-AI approach to analyzing human-AI interaction trends in registered clinical trials. The hybrid workflow comprised a frontier generative AI model (GPT-5.5) and human review to screen and categorize records returned by an AI-focused search. The findings indicate a marked increase in AI-related trials over time, with recent gro
The proliferation of advanced generative AI models (like GPT-5.5 referenced) allows for sophisticated hybrid human-AI research methods, making this analysis feasible and timely.
This paper highlights the accelerating integration of AI into a critical and highly regulated sector like clinical trials, indicating a broader trend of AI adoption across specialized domains.
The methodology demonstrates an effective way to track and analyze AI's impact using AI itself, providing a template for future research into transformative technologies.
- · AI developers
- · Clinical research organizations
- · Healthcare technology startups
- · Patients (indirectly)
- · Traditional clinical research methods
- · Manual data analysis services
Increased efficiency and potentially improved outcomes in clinical trial design and execution.
New regulatory frameworks and ethical guidelines will be required to manage AI oversight in healthcare.
The acceleration of new drug and therapy development could lead to a re-evaluation of healthcare economic models.
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Read at arXiv cs.AI