The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases

Abstract

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.

Paper invited for the first issue of Meta-Radiology

References in corpus (7)

• On the Opportunities and Risks of Foundation Models
• Sparks of Artificial General Intelligence: Early experiments with GPT-4
• The Medical Segmentation Decathlon
• Emergent Abilities of Large Language Models
• MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
• How I failed machine learning in medical imaging -- shortcomings and recommendations
• Towards Expert-Level Medical Question Answering with Large Language Models

Cited by in corpus (2)

• Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability
• MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answering