ChatGPT as a Support Tool for Informed Consent and Preoperative Patient Education Prior to Penile Prosthesis Implantation
<b>Background/Objectives</b>: Artificial intelligence (AI), particularly natural language processing (NLP) models such as ChatGPT, presents novel opportunities for patient education and informed consent. This study evaluated ChatGPT's use as a support tool for informed consent before penile prosthesis implantation (PPI) in patients with erectile dysfunction (ED) following radical prostatectomy. <b>Methods</b>: ChatGPT-4 answered 20 frequently asked questions across four categories: ED and treatment, PPI surgery, complications, and postoperative care. Three senior urologists independently rated information quality using the DISCERN instrument on a Likert scale ranging from 1 (poor quality) to 5 (good quality). Readability was assessed using the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) formulas, and inter-rater reliability was measured using intraclass correlation coefficients. <b>Results</b>: The inter-rater reliability coefficient was 0.76 (95% CI 0.71-0.80). Mean DISCERN scores indicated moderate quality: 2.79 ± 0.92 for ED and treatment, 2.57 ± 0.98 for surgery, 2.65 ± 0.86 for complications, and 2.74 ± 0.90 for postoperative care. High scores (>4) were achieved for clarity and relevance, while complex issues, such as risks and alternative treatments, scored the lowest (<2). The FRE scores ranged from 9.8 to 28.39, and FKGL scores ranged from 14.04 to 17.41, indicating complex readability suitable for college-level comprehension. <b>Conclusions</b>: ChatGPT currently provides variable and often inadequate quality information without sufficient comprehensibility for informed patient decisions, indicating the need for further improvements in quality and readability.
Authors
- Thorsten Schlomm (ORCID: https://orcid.org/0000-0001-9557-4653)
- Thomas Kurz (ORCID: https://orcid.org/0000-0002-1411-3394)
- Sebastian Hofbauer (ORCID: https://orcid.org/0000-0002-1796-9905)
- Robert W. Peters (ORCID: https://orcid.org/0000-0003-1699-0419)
- Isabel Lichy (ORCID: https://orcid.org/0000-0002-1680-3604)
- Jacob Schmidt (ORCID: https://orcid.org/0009-0002-0319-5002)
- Bernhard Ralla (ORCID: https://orcid.org/0000-0001-7226-3852)
- Henning Plage (ORCID: https://orcid.org/0000-0002-7680-1870)
- Jonathan Jeutner (ORCID: https://orcid.org/0009-0009-7035-9127)
- J. Neymeyer
Institutions
- Humboldt-Universität zu Berlin (DE)
- Helios Hospital Berlin-Buch (DE)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Journal of Clinical Medicine
- Published
- 2024-12-10
- DOI
- https://doi.org/10.3390/jcm13247482
- Citations
- 8
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.96