ChatGPT from radiologists’ perspective

Şendur, Halit Nahit; Şendur, Aylin Billur; Cerit, Mahi Nur

doi:10.1259/bjr.20230203

Cited by 11 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IM interactions among a professional workforce are found to support task completion, accuracy, and quality of outcomes [69]. Historically CA were powered by rules-based systems or 'small' AI language models, while modern CA like ChatGPT are using Large Language Models (LLM) [64][65][66][67][68][71][72][73][74].…”

Section: Instant Messaging and Conversational Agents (Ca) In Healthcarementioning

confidence: 99%

Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study

Burns,

Gichoya,

Kohli

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

Radiology specific clinical decision support systems (CDSS) and artificial intelligence are poorly integrated into the radiologist workflow. Current research and development efforts of radiology CDSS focus on 4 main interventions, based around exam centric time points–after image acquisition, intra-report support, post-report analysis, and radiology workflow adjacent. We review the literature surrounding CDSS tools in these time points, requirements for CDSS workflow augmentation, and technologies that support clinician to computer workflow augmentation. We develop a theory of radiologist-decision tool interaction using a sequential explanatory study design. The study consists of 2 phases, the first a quantitative survey and the second a qualitative interview study. The phase 1 survey identifies differences between average users and radiologist users in software interventions using the User Acceptance of Information Technology: Toward a Unified View (UTAUT) framework. Phase 2 semi-structured interviews provide narratives on why these differences are found. To build this theory, we propose a novel solution called Radibot—a conversational agent capable of engaging clinicians with CDSS as an assistant using existing instant messaging systems supporting hospital communications. This work contributes an understanding of how radiologist-users differ from the average user and can be utilized by software developers to increase satisfaction of CDSS tools within radiology.

show abstract

Section: Instant Messaging and Conversational Agents (Ca) In Healthcarementioning

confidence: 99%

Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study

Burns,

Gichoya,

Kohli

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

show abstract

Section: Large Language Modelmentioning

confidence: 99%

“…Large language models are gaining popularity as they possess the ability to generate human-like text [58]. These models have captured the interest of the scientific community due to their potential applications in clinical decision support and academic writing [58]. These chatbot models leverage natural language processing to analyze inquiries and automate responses, simulating human conversation [59].…”

Section: Large Language Modelmentioning

confidence: 99%

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Kim,

Gong,

Bang

2023

Biomimetics

View full text Add to dashboard Cite

The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.

show abstract

“…ChatGPT can generate a decent automated radiology report [ 3 ]. The radiologist must understand a few essential points before using ChatGPT for radiology report writing tasks.…”

Section: Editorialmentioning

confidence: 99%

ChatGPT From the Perspective of an Academic Oral and Maxillofacial Radiologist

Khurana¹,

Vaddi²

2023

Cureus

View full text Add to dashboard Cite

Chat Generative Pre-Trained Transformer (ChatGPT) is an open artificial intelligence (AI)-powered chatbot with various clinical and academic dentistry applications, including oral and maxillofacial radiology (OMFR). The applications can be extended to generating documents such as oral radiology reports if appropriate prompts are given. There are various challenges associated with this task. Like other fields, ChatGPT can be incorporated to generate content and answer oral radiology-related multiple-choice questions. However, its performance is limited to answering image-based questions. ChatGPT can help in scientific writing but can not be designated as an author due to the lack of validity of the content. This editorial outlines the potential applications and limitations of the current version of ChatGPT in OMFR academic settings.

show abstract

ChatGPT from radiologists’ perspective

Cited by 11 publications

References 13 publications

Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study

Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

ChatGPT From the Perspective of an Academic Oral and Maxillofacial Radiologist

Contact Info

Product

Resources

About