Optimizing patient understanding of spine MRI reports using AI: A prospective single center study

Interventional Pain Medicine Pub Date : 2025-02-18 DOI:10.1016/j.inpm.2025.100550

Sebastian Encalada , Sahil Gupta , Christine Hunt , Jason Eldrige , John Evans II , Johanna Mosquera-Moscoso , Laura Furtado Pessoa de Mendonca , Sharima Kanahan-Osman , Sohail Bade , Sahil Bade , Lisbet Ivicic , Stephanie Foskey , Jason Lyles , Juan Suarez , Aaron Fisher , Hamaad Khan , Jeffrey A. Stone , Mark Hurdle

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Abstract

Background

Patient comprehension of spine MRI reports remains a significant challenge, potentially affecting healthcare engagement and outcomes. Artificial Intelligence (AI) may offer a solution for interpreting complex medical terminology into layman's terms language.

Objective

To evaluate the effectiveness of AI-based interpretation of spine MRI reports in improving patient comprehension and satisfaction.

Methods

A prospective, single-center survey study was conducted at a single institution's multidisciplinary pain and spine clinics from May 2024 to November 2024, enrolling 102 adult patients scheduled for spine MRI. Imaging reports were interpreted using a single AI-based Large Language Model (LLM) that is securely operated within the hospital's network, with interpretations independently reviewed by healthcare providers and research coordinators. A board-certified neuroradiologist evaluated the accuracy of AI interpretations using a standardized 5-point scale. We analyzed survey responses from participants who received both their original MRI reports and AI-interpreted versions, comparing comprehension, clarity, engagement, and satisfaction.

Results

Participants reported higher comprehension with AI-interpreted MRI reports versus original radiology reports (8.50 ± 1.91 vs 6.56 ± 2.42; P < .001). AI interpretations received superior scores for clarity (8.57 ± 1.79 vs 6.96 ± 2.12; P < .001), understanding of medical conditions (7.75 ± 2.18 vs 6.27 ± 2.28; P < .001), and healthcare engagement (8.35 ± 2.00 vs 6.78 ± 2.48; P < .001). Accuracy assessment showed that 82.4 % of AI interpretations achieved high-quality ratings (≥4) [95 % CI: 69.7%–90.4 %], while 92.2 % were rated acceptable (≥3). Most participants (54.0 %) assigned the highest possible recommendation scores to AI interpretation. No significant differences were found between age groups and gender.

Conclusions

AI-based interpretation of spine MRI reports significantly improved patient comprehension and satisfaction. Despite the promise of rapidly evolving AI-based technologies, a considerable percentage of AI interpretations were deemed to be inaccurate, warranting the need for further research.

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利用人工智能优化患者对脊柱MRI报告的理解：一项前瞻性单中心研究

背景：患者对脊柱MRI报告的理解仍然是一个重大挑战，可能影响医疗保健的参与和结果。人工智能（AI）可以为将复杂的医学术语解释为外行人的术语语言提供解决方案。目的评价基于人工智能的脊柱MRI报告解读在提高患者理解力和满意度方面的有效性。方法于2024年5月至2024年11月在一家机构的多学科疼痛和脊柱诊所进行了一项前瞻性、单中心调查研究，纳入了102名计划进行脊柱MRI检查的成年患者。成像报告使用单一的基于人工智能的大型语言模型（LLM）进行解释，该模型在医院网络中安全运行，由医疗保健提供者和研究协调员独立审查解释。一位委员会认证的神经放射学家使用标准化的5分制评估了人工智能解释的准确性。我们分析了收到原始MRI报告和人工智能解释版本的参与者的调查反馈，比较了理解、清晰度、参与度和满意度。结果与原始放射学报告相比，参与者对人工智能解释的MRI报告的理解程度更高(8.50±1.91 vs 6.56±2.42；P & lt;措施)。人工智能口译在清晰度方面得分更高（8.57±1.79 vs 6.96±2.12）；P & lt;.001)，对医疗状况的理解(7.75±2.18 vs 6.27±2.28；P & lt;.001)，医疗敬业度(8.35±2.00 vs 6.78±2.48；P & lt;措施)。准确性评估显示，82.4%的人工智能口译达到了高质量评分（≥4）[95% CI: 69.7% - 90.4%]，而92.2%的人工智能口译被评为可接受（≥3）。大多数参与者（54.0%）给AI解释给出了最高的推荐分数。年龄和性别之间没有明显差异。结论基于ai的脊柱MRI报告解读可显著提高患者的理解力和满意度。尽管基于人工智能的技术有望迅速发展，但相当大比例的人工智能解释被认为是不准确的，需要进一步研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Interventional Pain Medicine

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