Potential Use Cases for ChatGPT in Radiology Reporting

Elkassem, Asser Abou; Smith, Andrew D.

doi:10.2214/ajr.23.29198

Cited by 81 publications

(32 citation statements)

References 1 publication

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“…Those tasks represented a sample of proposed LLM uses that were synthesized from the literature and included, but were not limited to, optimizing alerts for clinical decision support, providing a differential diagnosis, writing a discharge summary, recommending treatment options, translating radiology reports into layperson language, writing scientific manuscripts, and generating personalized study plans for students or trainees among others. 2,[6][7][8][15][16][17][18][19][20][21][22][23][24][25] We distributed the proposed clinical practice tasks over two questions, and had one question for research tasks and one question for education tasks. The purpose of this section was to measure the appropriateness of these tasks by category and to determine if any individual tasks were negative or positive outliers.…”

Section: Study Design and Samplingmentioning

confidence: 99%

A Survey of Clinicians' Views of the Utility of Large Language Models

Spotnitz,

Idnay,

Gordon

et al. 2024

Appl Clin Inform

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Objective: Large language models (LLMs) like ChatGPT are powerful algorithms that have been shown to produce human-like text from input data. A number of potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed healthcare providers for their opinions about whether the technology is fit for use. Materials and Methods: We distributed a validated mixed-methods survey to gauge practicing clinicians’ comfort with LLMs for a breadth of tasks in clinical practice, research and education, which were selected from the literature. Results: A total of 30 clinicians fully completed the survey. Of the 23 tasks, 16 were rated positively by more than 50% of the respondents. Based on our qualitative analysis, healthcare providers considered LLMs to have excellent synthesis skills and efficiency. However, our respondents had concerns that LLMs could generate false information and propagate training data bias. Discussion: Our survey respondents were most comfortable with scenarios that allow LLMs to function in an assistive role, like a physician extender or trainee. Conclusion: In a uniquely rigorous and comprehensive mixed-methods survey of clinicians about LLM use, healthcare providers were encouraging of having LLMs in healthcare for many tasks, and especially in assistive roles. There is a need for continued human-centered development of both LLMs and artificial intelligence (AI) in general.

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Section: Study Design and Samplingmentioning

confidence: 99%

A Survey of Clinicians' Views of the Utility of Large Language Models

Spotnitz,

Idnay,

Gordon

et al. 2024

Appl Clin Inform

View full text Add to dashboard Cite

show abstract

“…While originally designed to facilitate interactive dialogue, the integration of ChatGPT in various disciplines, including radiology, has presented compelling opportunities 6,7 . By harnessing its language comprehension capabilities and extensive knowledge base, ChatGPT has the potential to revolutionize how radiologists interact with medical data and information 6…”

Section: Introductionmentioning

confidence: 99%

“…The potential uses of ChatGPT in the field of radiology are particularly promising 7,8 . Radiology involves the use of a wide variety of imaging techniques to aid in diagnosis and intervention planning, generating vast amounts of imaging data and reports 1 .…”

Section: Introductionmentioning

confidence: 99%

Current applications and future potential of ChatGPT in radiology: A systematic review

Temperley,

O'Sullivan,

Mac Curtain

et al. 2024

J Med Imag Rad Onc

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SummaryThis study aimed to comprehensively evaluate the current utilization and future potential of ChatGPT, an AI‐based chat model, in the field of radiology. The primary focus is on its role in enhancing decision‐making processes, optimizing workflow efficiency, and fostering interdisciplinary collaboration and teaching within healthcare. A systematic search was conducted in PubMed, EMBASE and Web of Science databases. Key aspects, such as its impact on complex decision‐making, workflow enhancement and collaboration, were assessed. Limitations and challenges associated with ChatGPT implementation were also examined. Overall, six studies met the inclusion criteria and were included in our analysis. All studies were prospective in nature. A total of 551 chatGPT (version 3.0 to 4.0) assessment events were included in our analysis. Considering the generation of academic papers, ChatGPT was found to output data inaccuracies 80% of the time. When ChatGPT was asked questions regarding common interventional radiology procedures, it contained entirely incorrect information 45% of the time. ChatGPT was seen to better answer US board‐style questions when lower order thinking was required (P = 0.002). Improvements were seen between chatGPT 3.5 and 4.0 in regard to imaging questions with accuracy rates of 61 versus 85%(P = 0.009). ChatGPT was observed to have an average translational ability score of 4.27/5 on the Likert scale regarding CT and MRI findings. ChatGPT demonstrates substantial potential to augment decision‐making and optimizing workflow. While ChatGPT's promise is evident, thorough evaluation and validation are imperative before widespread adoption in the field of radiology.

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“…Recent progress in natural language processing has led to the development of large language models (such as GPT-4) capable of generating high-quality text. GPT-4 has gained widespread attention due to its ability to generate coherent and informative text on various topics (1)(2)(3)(4). However, there is a need to determine its ability to write convincing medical research articles.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing GPT-4-generated Radiology Abstracts from Original Abstracts: Performance of Blinded Human Observers and AI Content Detector

Ufuk

Peker

Sağtaş

et al. 2023

Preprint

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Objective: To determine GPT-4's effectiveness in writing scientific radiology article abstracts and investigate human reviewers' and AI Content detectors' success in distinguishing these abstracts. Additionally, to determine the similarity scores of abstracts generated by GPT-4 to better understand its ability to create unique text. Methods: The study collected 250 original articles published between 2021 and 2023 in five radiology journals. The articles were randomly selected, and their abstracts were generated by GPT-4 using a specific prompt. Three experienced academic radiologists independently evaluated the GPT-4 generated and original abstracts to distinguish them as original or generated by GPT-4. All abstracts were also uploaded to an AI Content Detector and plagiarism detector to calculate similarity scores. Statistical analysis was performed to determine discrimination performance and similarity scores. Results: Out of 134 GPT-4 generated abstracts, average of 75 (56%) were detected by reviewers, and average of 50 (43%) original abstracts were falsely categorized as GPT-4 generated abstracts by reviewers. The sensitivity, specificity, accuracy, PPV, and NPV of observers in distinguishing GPT-4 written abstracts ranged from 51.5% to 55.6%, 56.1% to 70%, 54.8% to 60.8%, 41.2% to 76.7%, and 47% to 62.7%, respectively. No significant difference was observed between observers in discrimination performance. Conclusion: GPT-4 can generate convincing scientific radiology article abstracts. However, human reviewers and AI Content detectors have difficulty in distinguishing GPT-4 generated abstracts from original ones.

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Potential Use Cases for ChatGPT in Radiology Reporting

Cited by 81 publications

References 1 publication

A Survey of Clinicians' Views of the Utility of Large Language Models

A Survey of Clinicians' Views of the Utility of Large Language Models

Current applications and future potential of ChatGPT in radiology: A systematic review

Distinguishing GPT-4-generated Radiology Abstracts from Original Abstracts: Performance of Blinded Human Observers and AI Content Detector

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