The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders:
Perspectives and Use Cases
- URL: http://arxiv.org/abs/2306.06767v2
- Date: Thu, 6 Jul 2023 14:54:22 GMT
- Title: The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders:
Perspectives and Use Cases
- Authors: Jiancheng Yang, Hongwei Bran Li, Donglai Wei
- Abstract summary: This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging.
The paper introduces an analytic framework for presenting the complex interactions between LLMs and the broader ecosystem of medical imaging stakeholders.
- Score: 9.488544611843073
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- The Role of Language Models in Modern Healthcare: A Comprehensive Review [2.048226951354646]
The application of large language models (LLMs) in healthcare has gained significant attention.
This review examines the trajectory of language models from their early stages to the current state-of-the-art LLMs.
arXiv Detail & Related papers (2024-09-25T12:15:15Z) - From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice [11.196196955468992]
Large Language Models (LLMs) have rapidly evolved from text-based systems to multimodal platforms.
We examine the current landscape of MLLMs in healthcare, analyzing their applications across clinical decision support, medical imaging, patient engagement, and research.
arXiv Detail & Related papers (2024-09-14T02:35:29Z) - Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health [1.8772687384996551]
Large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI.
We showcase the power of LLMs in handling unstructured conversational data through four case studies.
arXiv Detail & Related papers (2024-06-19T16:02:04Z) - Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care [0.196629787330046]
This study delves into the impact of cutting-edge Large Language Models (LLMs) on medical diagnostics, with a keen focus on the dental sector.
The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery.
It critically assesses the broad implications and challenges within various sectors, including academia and healthcare.
arXiv Detail & Related papers (2024-06-07T06:44:09Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - Improving Medical Dialogue Generation with Abstract Meaning
Representations [26.97253577302195]
Medical Dialogue Generation serves a critical role in telemedicine by facilitating the dissemination of medical expertise to patients.
Existing studies focus on incorporating textual representations, which have limited their ability to represent the semantics of text.
We introduce the use of Abstract Meaning Representations (AMR) to construct graphical representations that delineate the roles of language constituents and medical entities.
arXiv Detail & Related papers (2023-09-19T13:31:49Z) - XrayGPT: Chest Radiographs Summarization using Medical Vision-Language
Models [60.437091462613544]
We introduce XrayGPT, a novel conversational medical vision-language model.
It can analyze and answer open-ended questions about chest radiographs.
We generate 217k interactive and high-quality summaries from free-text radiology reports.
arXiv Detail & Related papers (2023-06-13T17:59:59Z) - Artificial General Intelligence for Medical Imaging Analysis [92.3940918983821]
Large-scale Artificial General Intelligence (AGI) models have achieved unprecedented success in a variety of general domain tasks.
These models face notable challenges arising from the medical field's inherent complexities and unique characteristics.
This review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.
arXiv Detail & Related papers (2023-06-08T18:04:13Z) - ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using
Large Language Models [53.73049253535025]
Large language models (LLMs) have recently demonstrated their potential in clinical applications.
This paper presents a method for integrating LLMs into medical-image CAD networks.
The goal is to merge the strengths of LLMs' medical domain knowledge and logical reasoning with the vision understanding capability of existing medical-image CAD models.
arXiv Detail & Related papers (2023-02-14T18:54:06Z) - Informing clinical assessment by contextualizing post-hoc explanations
of risk prediction models in type-2 diabetes [50.8044927215346]
We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state.
We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability.
Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
arXiv Detail & Related papers (2023-02-11T18:07:11Z) - VBridge: Connecting the Dots Between Features, Explanations, and Data
for Healthcare Models [85.4333256782337]
VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow.
We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence.
We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
arXiv Detail & Related papers (2021-08-04T17:34:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.