The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 10 of its 45 references that are in the map. First posted on arXiv 11 Jun 2023.
Builds on
10 of 45 references.
References · 10
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Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.
7 papers.
- 2023ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Datacited by 14
- 2024Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challengecited by 14
- 2025DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Taskscited by 12
- 2024Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliabilitycited by 6
- 2025MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answeringcited by 5
- 2025AI in Money Matterscited by 0
- 2026Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settingscited by 0
- 2026Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings7 May 2026
- 2025MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answering28 Jun 2025
- 2025AI in Money Matters12 May 2025
- 2025DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks25 Feb 2025
- 2024Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge14 Feb 2024
- 2024Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability15 Jan 2024
- 2023ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data8 Oct 2023