ChatGPT fails challenging the recent ESCMID brain abscess guideline

Dyckhoff-Shen, Susanne ; Koedel, Uwe; Brouwer, Matthijs C.; Bodilsen, Jacob und Klein, Matthias (2024): ChatGPT fails challenging the recent ESCMID brain abscess guideline. In: Journal of Neurology, Bd. 271, Nr. 4: S. 2086-2101

Abstract

Background : With artificial intelligence (AI) on the rise, it remains unclear if AI is able to professionally evaluate medical research and give scientifically valid recommendations. Aim : This study aimed to assess the accuracy of ChatGPT’s responses to ten key questions on brain abscess diagnostics and treatment in comparison to the guideline recently published by the European Society for Clinical Microbiology and Infectious Diseases (ESCMID). Methods : All ten PECO (Population, Exposure, Comparator, Outcome) questions which had been developed during the guideline process were presented directly to ChatGPT. Next, ChatGPT was additionally fed with data from studies selected for each PECO question by the ESCMID committee. AI’s responses were subsequently compared with the recommendations of the ESCMID guideline. Results : For 17 out of 20 challenges, ChatGPT was able to give recommendations on the management of patients with brain abscess, including grade of evidence and strength of recommendation. Without data prompting, 70% of questions were answered very similar to the guideline recommendation. In the answers that differed from the guideline recommendations, no patient hazard was present. Data input slightly improved the clarity of ChatGPT’s recommendations, but, however, led to less correct answers including two recommendations that directly contradicted the guideline, being associated with the possibility of a hazard to the patient. Conclusion : ChatGPT seems to be able to rapidly gather information on brain abscesses and give recommendations on key questions about their management in most cases. Nevertheless, single responses could possibly harm the patients. Thus, the expertise of an expert committee remains inevitable.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Klinikum der LMU München > Neurochirurgische Klinik und Poliklinik
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 0340-5354
• Sprache: Englisch