Trial Files: Leveraging large language models to summarize practice-changing clinical trials for clinicians.

Developing appropriate policies, practices, and behaviors for new technologies is crucial for protecting and improving health care system performance. However, identifying the types and magnitude of relevant consequences can be challenging in the early stages of technological development and implementation. For example, electronic medical records were meant to provide high-quality documentation but were also associated with disrupted clinical workflow and increased provider burnout [,]. Designing policies that mitigate the harms and maximize the benefits from modern large language models (LLMs) can be similarly challenging. LLMs are a class of artificial intelligence systems trained on vast text datasets to understand and generate human-like language. In healthcare, their ability to process unstructured clinical information positions them as emerging tools for documentation, decision support, patient communication, and administrative workflows. Their wide-ranging capacity can help support clerical tasks, such as summarizing patient visits and drafting letters to patients []. They can also support diagnostic and therapeutic tasks, such as taking a patient’s history, interpreting certain investigations, recommending potential diagnoses, and suggesting investigations and therapies. In some cases, LLMs’ diagnostic performance may at times rival that of some human specialists [–]. These studies suggest promising performance in many contexts; however, this research is preliminary and subject to important limitations given the novelty of the field. Additionally, LLMs can also perpetuate biases, generate false information, and potentially displace humans from their jobs [,]. LLMs’ rapid development and intersection with multiple spheres of behavioral norms further complicate their assessment [–]. Studying how physicians consider the diverse benefits and risks can be particularly enlightening. First, their roles cross both leadership and frontline service provision. Second, they have to weigh the need to introduce advanced technologies for patient benefit against their potential harms [,]. Lastly, physicians using LLMs can improve their service quality and efficiency, but excessive reliance on LLMs can undermine physicians’ skills and long-term importance in the health care service market.