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KMID : 0191120230380380319
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Journal of Korean Medical Science
2023 Volume.38 No. 38 p.319 ~ p.319
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GPTZero Performance in Identifying Artificial Intelligence-Generated Medical Texts: A Preliminary Study
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Farrokh Habibzadeh
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Abstract
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Background : With emergence of chatbots to help authors with scientific writings, editors should have tools to identify artificial intelligence-generated texts. GPTZero is among the first websites that has sought media attention claiming to differentiate machine-generated from human-written texts.
Methods : Using 20 text pieces generated by ChatGPT in response to arbitrary questions on various topics in medicine and 30 pieces chosen from previously published medical articles, the performance of GPTZero was assessed.
Results : GPTZero had a sensitivity of 0.65 (95% confidence interval, 0.41?0.85); specificity, 0.90 (0.73?0.98); accuracy, 0.80 (0.66?0.90); and positive and negative likelihood ratios, 6.5 (2.1?19.9) and 0.4 (0.2?0.7), respectively.
Conclusion : GPTZero has a low false-positive (classifying a human-written text as machine-generated) and a high false-negative rate (classifying a machine-generated text as human-written).
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KEYWORD
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Scientific Writing, Journalism, Artificial Intelligence, Classification, Sensitivity and Specificity
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