The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases
It addresses the integration of AI into healthcare for medical imaging stakeholders, but it is incremental as it synthesizes existing perspectives without new empirical results.
This study explores how Large Language Models like ChatGPT can transform medical imaging by augmenting radiologists' skills, improving communication, and streamlining workflows, using an analytic framework to analyze interactions with stakeholders such as businesses and hospitals.
This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging. With the aid of public data, these models, which possess remarkable language understanding and generation capabilities, are augmenting the interpretive skills of radiologists, enhancing patient-physician communication, and streamlining clinical workflows. The paper introduces an analytic framework for presenting the complex interactions between LLMs and the broader ecosystem of medical imaging stakeholders, including businesses, insurance entities, governments, research institutions, and hospitals (nicknamed BIGR-H). Through detailed analyses, illustrative use cases, and discussions on the broader implications and future directions, this perspective seeks to raise discussion in strategic planning and decision-making in the era of AI-enabled healthcare.