Reliability, Accuracy, and Comprehensibility of AI-Based Responses to Common Patient Questions Regarding Spinal Cord Stimulation
<b>Background:</b> Although spinal cord stimulation (SCS) is an effective treatment for managing chronic pain, many patients have understandable questions and concerns regarding this therapy. Artificial intelligence (AI) has shown promise in delivering patient education in healthcare. This study evaluates the reliability, accuracy, and comprehensibility of ChatGPT's responses to common patient inquiries about SCS. <b>Methods:</b> Thirteen commonly asked questions regarding SCS were selected based on the authors' clinical experience managing chronic pain patients and a targeted review of patient education materials and relevant medical literature. The questions were prioritized based on their frequency in patient consultations, relevance to decision-making about SCS, and the complexity of the information typically required to comprehensively address the questions. These questions spanned three domains: pre-procedural, intra-procedural, and post-procedural concerns. Responses were generated using GPT-4.0 with the prompt "<i>If you were a physician, how would you answer a patient asking</i>…". Responses were independently assessed by 10 pain physicians and two non-healthcare professionals using a Likert scale for reliability (1-6 points), accuracy (1-3 points), and comprehensibility (1-3 points). <b>Results:</b> ChatGPT's responses demonstrated strong reliability (5.1 ± 0.7) and comprehensibility (2.8 ± 0.2), with 92% and 98% of responses, respectively, meeting or exceeding our predefined thresholds. Accuracy was 2.7 ± 0.3, with 95% of responses rated sufficiently accurate. General queries, such as "<i>What is spinal cord stimulation</i>?" and "<i>What are the risks and benefits</i>?", received higher scores compared to technical questions like "<i>What are the different types of waveforms used in SCS</i>?". <b>Conclusions:</b> ChatGPT can be implemented as a supplementary tool for patient education, particularly in addressing general and procedural queries about SCS. However, the AI's performance was less robust in addressing highly technical or nuanced questions.
Authors
- Marco Cascella (ORCID: https://orcid.org/0000-0002-5236-3132)
- Christopher Louis Robinson (ORCID: https://orcid.org/0000-0002-6276-9056)
- Giuliano Lo Bianco (ORCID: https://orcid.org/0000-0003-2705-1548)
- Leonardo Kapural (ORCID: https://orcid.org/0000-0003-2718-5837)
- Emanuele Sinagra (ORCID: https://orcid.org/0000-0002-8528-0384)
- Sean Li (ORCID: https://orcid.org/0000-0002-0492-1179)
- Miles Day
Institutions
- Texas Tech University (US)
- Brigham and Women's Hospital (US)
- University of Salerno (IT)
- Harvard University (US)
- Carolinas Pain Institute (US)
- Fondazione Istituto G. Giglio di Cefalù (IT)
- International Spine, Pain & Performance (US)
- Texas Tech University Health Sciences Center (US)
Publication Details
- Journal
- Journal of Clinical Medicine
- Published
- 2025-02-21
- DOI
- https://doi.org/10.3390/jcm14051453
- Citations
- 8
- Primary Topic
- Pain Management and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 17.53