Comparison of emergency medicine specialist, cardiologist, and chat-GPT in electrocardiography assessment

Günay, Serkan; Öztürk, Ahmet; Özerol, Hakan; Yiğit, Yavuz; Erenler, Ali Kemal

doi:10.1016/j.ajem.2024.03.017

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The authors advised against unsupervised patient use and advocated for efforts to improve diagnostic and triage accuracy. Further studies highlighting ChatGPT's potential in emergency medicine include its role in suggesting diagnostic imaging [52] and providing diagnostic recommendations based on electrocardiography data [53]. Given that hand injuries are often first seen and evaluated in the ED [1,23,54], equipping emergency providers with resources like LLMs can help expedite triage and diagnostic workup.…”

Section: Discussionmentioning

confidence: 99%

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries

Pressman,

Borna,

Gomez-Cabello

et al. 2024

JCM

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OpenAI’s ChatGPT (San Francisco, CA, USA) and Google’s Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice.

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Section: Discussionmentioning

confidence: 99%

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries

Pressman,

Borna,

Gomez-Cabello

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

“…There is an increasing interest in employing LLMs in cardiovascular diseases such as the assessment of electrocardiography and answering questions about acute coronary syndrome regarding European Society of Cardiology Guidelines. 2 , 3 Additionally, LLMs have been utilized for simplifying cardiovascular magnetic resonance reports in cardiovascular diseases. 4 Although AI can help in cardiovascular diseases, it is vital to evaluate the diagnostic performance of these LLMs.…”

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confidence: 99%

Large Language Models: Could They Be the Next Generation of Clinical Decision Support Systems in Cardiovascular Diseases?

Güneş

2024

Anatol J Cardiol

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Did GPT-4 really perform electrocardiography assessment?

Niset,

El Hadwe,

Barrit

2024

The American Journal of Emergency Medicine

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Comparison of emergency medicine specialist, cardiologist, and chat-GPT in electrocardiography assessment

Cited by 6 publications

References 13 publications

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries

Large Language Models: Could They Be the Next Generation of Clinical Decision Support Systems in Cardiovascular Diseases?

Did GPT-4 really perform electrocardiography assessment?

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