A commentary on “Emergency surgery in the era of artificial intelligence: ChatGPT could be the doctor’s right-hand man”

Abavisani, Mohammad; Dadgar, Farhad; Karbalaei, Mohsen

doi:10.1097/js9.0000000000000561

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…Furthermore, ChatGPT can offer personalized treatment recommendations based on specific patient information, such as age, gender, and genetic background, crucial for enhancing treatment effectiveness and predicting disease progression. In emergency situations, such as acute infections or trauma, ChatGPT’s rapid response capability can provide immediate support to physicians’ decision-making, which is especially crucial in emergency and critical care medicine 3 . Importantly, by offering data-driven recommendations, ChatGPT helps reduce diagnostic errors, especially when physicians face high workloads or lack experience.…”

mentioning

confidence: 99%

Application of ChatGPT in pediatric surgery: opportunities and challenges

Mao,

Bao,

Yang

et al. 2024

International Journal of Surgery

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confidence: 99%

Application of ChatGPT in pediatric surgery: opportunities and challenges

Mao,

Bao,

Yang

et al. 2024

International Journal of Surgery

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A commentary on “advances in artificial intelligence (AI) based diagnosis and treatment of liver diseases – correspondence”

Abavisani,

Dadgar,

Peikfalak

et al. 2023

International Journal of Surgery

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The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

Preiksaitis,

Ashenburg,

Bunney

et al. 2024

JMIR Med Inform

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Background Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. Objective Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs’ potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. Methods Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs’ use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. Results A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs’ outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs’ capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. Conclusions LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians’ AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

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A commentary on “Emergency surgery in the era of artificial intelligence: ChatGPT could be the doctor’s right-hand man”

Cited by 4 publications

References 10 publications

Application of ChatGPT in pediatric surgery: opportunities and challenges

Application of ChatGPT in pediatric surgery: opportunities and challenges

A commentary on “advances in artificial intelligence (AI) based diagnosis and treatment of liver diseases – correspondence”

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

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