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

arXiv: https://arxiv.org/abs/2306.06767 · HTML: https://mlsanity.com/paper/2306.06767/

## 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 (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study](https://mlsanity.com/paper/2010.04049/) (2020)

## Built on by (most cited first, 7 of 7)

- [ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data](https://mlsanity.com/paper/2310.05242/) (2023)
- [Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge](https://mlsanity.com/paper/2402.09372/) (2024)
- [DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks](https://mlsanity.com/paper/2502.17764/) (2025)
- [Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability](https://mlsanity.com/paper/2401.07868/) (2024)
- [MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answering](https://mlsanity.com/paper/2506.22900/) (2025)
- [AI in Money Matters](https://mlsanity.com/paper/2505.07393/) (2025)
- [Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings](https://mlsanity.com/paper/2605.06594/) (2026)

## 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.