With a growing number of studies applying generative artificial intelligence (GAI) models for health purposes, reporting standards are being developed to guide authors in this space. We describe the currently available reporting guidelines that apply to GAI models and provide an overview of upcoming reporting standards. Investigators must remain up-to-date with the most applicable tools to guide the comprehensive reporting of their research as we integrate GAI in healthcare.

Chen et al. demonstrate that large language models (LLMs) frequently prioritize agreement over accuracy when responding to illogical medical prompts, a behavior known as sycophancy. By reinforcing user assumptions, this tendency may amplify misinformation and bias in clinical contexts. The authors find that simple prompting strategies and LLM fine-tuning can markedly reduce sycophancy without impairing performance, highlighting a path toward safer, more trustworthy applications of LLMs in medicine.

Biased and poorly documented dermatology datasets pose risks to the development of safe and generalizable artificial intelligence (AI) tools. We created a Dataset Nutrition Label (DNL) for multiple dermatology datasets to support transparent and responsible data use. The DNL offers a structured, digestible summary of key attributes, including metadata, limitations, and risks, enabling data users to better assess suitability and proactively address potential sources of bias in datasets.

In “A Randomized Controlled Trial of Mobile Intervention Using Health Support Bubbles to Prevent Social Frailty”, Hayashi et al. investigated the effects of using a mobile health app with family or individually. Greater improvements in social behavior and frailty were noted in participants who used the app with family. In an era of remote healthcare and app-based health interventions, Hayashi et al.’s study reminds of the importance of human connection.