mlsanityData to 14 Oct 2026

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

arXiv 2306.06767 ↗First posted 11 Jun 2023eess.IV cs.CL cs.CVcited by 7

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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

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  3. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  4. 2022Emergent Abilities of Large Language Modelscited by 2,512
  5. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  6. 2021The Medical Segmentation Decathloncited by 628
  7. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  8. 2020Artificial Intelligence, Values and Alignmentcited by 448
  9. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
  10. 2020Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Studycited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.