Optimizing patient understanding of spine MRI reports using AI: A prospective single center study
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.
Authors
- Jason S. Eldrige (ORCID: https://orcid.org/0000-0003-0250-6024)
- Juan Carlos Mantilla Súarez
- Huraira Alam Khan
- Aaron Fisher
- Sahil Bade (ORCID: https://orcid.org/0000-0003-4525-1914)
- J. Lyles
- Christine Hunt (ORCID: https://orcid.org/0000-0002-0507-7889)
- Sohail Bade (ORCID: https://orcid.org/0000-0002-1269-9301)
- Sebastián Encalada (ORCID: https://orcid.org/0000-0003-0657-6089)
- John L. Evans (ORCID: https://orcid.org/0000-0002-4034-4081)
- Stephanie Foskey
- Johanna Mosquera‐Moscoso (ORCID: https://orcid.org/0000-0002-8498-4910)
- Laura Furtado-Pessoa-de-Mendonca (ORCID: https://orcid.org/0000-0001-5827-8687)
- Sahil Gupta (ORCID: https://orcid.org/0000-0002-6902-9392)
- Mark Friedrich B. Hurdle
- Jim Stone
- Lisbet Ivicic
- Sharima Kanahan-Osman
Institutions
- Jacksonville College (US)
- WinnMed (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Interventional Pain Medicine
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1016/j.inpm.2025.100550
- Citations
- 4
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 2.62