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Large language models in radiology workflows: An exploratory study of generative AI for non-visual tasks in the
Stefanie Steinhauser1, Sabrina Welsch2
1Professor of Health Economy and Entrepreneurship, Technical University of Applied Sciences Amberg-Weiden, 92637 Weiden, Germany.
Background:
Large language models (LLMs) are gaining attention for their potential to enhance radiology workflows by addressing challenges such as increasing workloads and staff shortages. However, limited knowledge among radiologists and concerns about their practical implementation and ethical implications present challenges.
Objective:
This study investigates radiologists' perspectives on the use of LLMs, exploring their potential benefits, challenges, and impact on workflows and professional roles.
Methods:
An exploratory, qualitative study was conducted using 12 semi-structured interviews with radiology experts. Data were analyzed to assess participants' awareness, attitudes, and perceived applications of LLMs in radiology.
Results:
LLMs were identified as promising tools for reducing workloads by streamlining tasks like summarizing clinical histories and generating standardized reports, improving communication and efficiency. Participants expressed openness to LLM integration but noted concerns about their impact on human interaction, ethical standards, and liability. The role of radiologists is expected to evolve with LLM adoption, with a shift toward data stewardship and interprofessional collaboration. Barriers to implementation included limited awareness, regulatory constraints, and outdated infrastructure.
Conclusions:
The integration of LLMs is hindered by regulatory challenges, outdated infrastructure, and limited awareness among radiologists. Policymakers should establish clear, practical regulations to address liability and ethical concerns while ensuring compliance with privacy standards. Investments in modernizing clinical infrastructure and expanding training programs are critical to enable radiologists to effectively use these tools. By addressing these barriers, LLMs can enhance efficiency, reduce workloads, and improve patient care, while preserving the central role of radiologists in diagnostic and therapeutic processes.
