Updating the Minimum Information about CLinical Artificial Intelligence
  (MI-CLAIM) checklist for generative modeling research
Brenda Y. Miao Irene Y. Chen Christopher YK Williams Jaysón Davidson Augusto Garcia-Agundez Harry Sun Travis Zack Atul J. Butte Madhumita Sushil
Recent advances in generative models, including large language models (LLMs),
vision language models (VLMs), and diffusion models, have accelerated the field
of natural language and image processing in medicine and marked a significant
paradigm shift in how biomedical models can be developed and deployed. While
these models are highly adaptable to new tasks, scaling and evaluating their
usage presents new challenges not addressed in previous frameworks. In
particular, the ability of these models to produce useful outputs with little
to no specialized training data ("zero-" or "few-shot" approaches), as well as
the open-ended nature of their outputs, necessitate the development of updated
guidelines in using and evaluating these models. In response to gaps in
standards and best practices for the development of clinical AI tools
identified by US Executive Order 141103 and several emerging national networks
for clinical AI evaluation, we begin to formalize some of these guidelines by
building on the "Minimum information about clinical artificial intelligence
modeling" (MI-CLAIM) checklist. The MI-CLAIM checklist, originally developed in
2020, provided a set of six steps with guidelines on the minimum information
necessary to encourage transparent, reproducible research for artificial
intelligence (AI) in medicine. Here, we propose modifications to the original
checklist that highlight differences in training, evaluation, interpretability,
and reproducibility of generative models compared to traditional AI models for
clinical research. This updated checklist also seeks to clarify cohort
selection reporting and adds additional items on alignment with ethical
standards.