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
Santiago Nogué-Xarau1, José Ríos-Guillermo2, Montserrat Amigó-Tadín3 y Grupo de Trabajo de Toxicología de la Societat Catalana de Medicina d’Urgències i Emergències (SoCMUETox)
Objectives.
To present questions about poisoning to 4 artificial intelligence (AI) systems and 4 clinical toxicologists and determine whether readers can identify the source of the answers. To evaluate and compare text quality and level of knowledge found in the AI and toxicologists’ responses.
Methods.
Ten questions about toxicology were presented to the following AI systems: Copilot, Bard, Luzia, and ChatGPT. Four clinical toxicologists were asked to answer the same questions. Twenty-four recruited experts in toxicology were sent a pair of answers (1 from an AI system and one from a toxicologist) for each of the 10 questions. For each answer, the experts had to identify the source, evaluate text quality, and assess level of knowledge reflected. Quantitative variables were described as mean (SD) and qualitative ones as absolute frequency and proportion. A value of P < .05 was considered significant in all comparisons.
Results.
Of the 240 evaluated AI answers, the expert evaluators thought that 21 (8.8%) and 38 (15.8%), respectively, were certainly or probably written by a toxicologist. The experts were unable to guess the source of 13 (5.4%) AI answers. Luzia and ChatGPT were better able to mislead the experts than Bard (P = .036 and P = .041, respectively). Text quality was judged excellent in 38.8% of the AI answers. ChatGPT text quality was rated highest (61.3% excellent) vs Bard (34.4%), Luzia (31.7%), and Copilot (26.3%) (P < .001, all comparisons). The average score for the level of knowledge perceived in the AI answers was 7.23 (1.57) out of 10. The highest average score was achieved by ChatGPT at 8.03 (1.26) vs Luzia (7.02 [1,63]), Bard (6.91 [1.64]), and Copilot (6.91 [1.46]) (P < .001, all comparisons).
Conclusions.
Luzia and ChatGPT answers to the toxicology questions were often thought to resemble those of clinical toxicologists. ChatGPT answers were judged to be very well-written and reflect a very high level of knowledge.
