RESEARCH · RESEARCH · #1323
Multidimensional framework to classify human–AI interactions in clinical trials (arXiv:2609.38559v1)
This arXiv preprint proposes a multidimensional classification framework for human–AI interactions (HAIIs) in clinical-trial records, combining AI tasks, human–AI relationships, interaction configurations, and interacting human groups. The authors applied the scheme to 15 trial records, independently coded by two human reviewers and six LLM classifiers, finding the framework supports more consistent comparison and that LLM assistance shows promise while human judgment remains necessary where records are incomplete or ambiguous.
KEY POINTS
- This arXiv preprint proposes a multidimensional classification framework for human–AI interactions (HAIIs) in clinical-trial records, combining AI tasks, human–AI relationships, interaction configurations, and interacting human groups.
- The authors applied the scheme to 15 trial records, independently coded by two human reviewers and six LLM classifiers, finding the framework supports more consistent comparison and that LLM assistance shows promise while human judgment remains necessary where records are incomplete or ambiguous.
- A standardised, multidimensional HAII taxonomy can improve consistency in identifying, comparing and synthesising AI roles in clinical trials and inform oversight and reporting.
WHY IT MATTERS
A standardised, multidimensional HAII taxonomy can improve consistency in identifying, comparing and synthesising AI roles in clinical trials and inform oversight and reporting.