  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 new
guidelines for robust reporting of clinical generative model research. 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 original MI-CLAIM checklist. The new
checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training,
evaluation, interpretability, and reproducibility of new generative models
compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN
checklist also seeks to clarify cohort selection reporting with unstructured
clinical data and adds additional items on alignment with ethical standards for
clinical AI research.
