The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Can Artificial Intelligence Improve the Readability of Patient Education Materials?
Open accessAbstract
Background: The recommended readability of online health education materials is at or below the sixth- to eighth-grade level. Nevertheless, more than a decade of research has demonstrated that most online education materials pertaining to orthopaedic surgery do not meet these recommendations. The repeated evidence of this limited progress underscores that unaddressed barriers exist to improving readability, such as the added time and cost associated with writing easily readable materials that cover complex topics. Freely available artificial intelligence (AI) platforms might facilitate the conversion of patient-education materials at scale, but to our knowledge, this has not been evaluated in orthopaedic surgery. Questions/purposes: (1) Can a freely available AI dialogue platform rewrite orthopaedic patient education materials to reduce the required reading skill level from the high-school level to the sixth-grade level (which is approximately the median reading level in the United States)? (2) Were the converted materials accurate, and did they retain sufficient content detail to be informative as education materials for patients? Methods: Descriptions of lumbar disc herniation, scoliosis, and spinal stenosis, as well as TKA and THA, were identified from educational materials published online by orthopaedic surgery specialty organizations and leading orthopaedic institutions. The descriptions were entered into an AI dialogue platform with the prompt “translate to fifth-grade reading level” to convert each group of text at or below the sixth-grade reading level. The fifth-grade reading level was selected to account for potential variation in how readability is defined by the AI platform, given that there are several widely used preexisting methods for defining readability levels. The Flesch Reading Ease score and Flesch-Kincaid grade level were determined for each description before and after AI conversion. The time to convert was also recorded. Each education material and its respective conversion was reviewed for factual inaccuracies, and each conversion was reviewed for its retention of sufficient detail for intended use as a patient education document. Results: As presented to the public, the current descriptions of herniated lumbar disc, scoliosis, and stenosis had median (range) Flesch-Kincaid grade levels of 9.5 (9.1 to 10.5), 12.6 (10.8 to 15), and 10.9 (8 to 13.6), respectively. After conversion by the AI dialogue platform, the median Flesch-Kincaid grade level scores for herniated lumbar disc, scoliosis, and stenosis were 5.0 (3.3 to 8.2), 5.6 (4.1 to 7.3), and 6.9 (5 to 7.8), respectively. Similarly, descriptions of TKA and THA improved from 12.0 (11.2 to 13.5) to 6.3 (5.8 to 7.6) and from 11.6 (9.5 to 12.6) to 6.1 (5.4 to 7.1), respectively. The Flesch Reading Ease scores followed a similar trend. Seconds per sentence conversion was median 4.5 (3.3 to 4.9) and 4.5 (3.5 to 4.8) for spine conditions and arthroplasty, respectively. Evaluation of the materials that were converted for ease of reading still provided a sufficient level of nuance for patient education, and no factual errors or inaccuracies were identified. Conclusion: We found that a freely available AI dialogue platform can improve the reading accessibility of orthopaedic surgery online patient education materials to recommended levels quickly and effectively. Professional organizations and practices should determine whether their patient education materials exceed current recommended reading levels by using widely available measurement tools, and then apply an AI dialogue platform to facilitate converting their materials to more accessible levels if needed. Additional research is needed to determine whether this technology can be applied to additional materials meant to inform patients, such as surgical consent documents or postoperative instructions, and whether the methods presented here are applicable to non–English language materials.
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In the citing paper
“ChatGPT is limited by the lack of physical presence in the surgical environment as it cannot physically manipulate or adjust implants or surgical instruments.28)”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
Experimental — accuracy not yet measuredIn the citing paper
“By equipping patients with this knowledge, ChatGPT plays a vital role in facilitating informed decision-making, as patients can weigh the benefits and risks more confidently.”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
In the citing paper
“Furthermore, the empathetic and reassuring nature of ChatGPT interactions can help alleviate patient anxiety, foster trust, and strengthen doctor-patient relationships.28)”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
In the citing paper
“During orthopedic procedures, ChatGPT can act as a virtual surgical assistant by processing real-time data from various sources including surgical instruments and patient vital signs.”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
In the citing paper
“Simultaneously, ChatGPT can monitor patient vital signs, detect anomalies or deviations from the norm, and promptly alert the surgical team to potential complications.”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
In the citing paper
“This finding affirmed the potential of AI as a reliable tool in both diagnosis and treatment.2831)”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
In the citing paper
“In scenarios involving remote or collaborative surgeries, ChatGPT can facilitate communication and information exchange between the operating surgeon and remote experts, thereby enhancing overall surgical experience and outcome.”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
In the citing paper
“Importantly, virtual surgical simulations substantially reduce the risk of errors during surgery.”
From Application of ChatGPT for Orthopedic Surgeries and Patient Care (2024) · cites this paper as [28]
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Citations
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Generating scholarly content with ChatGPT: Ethical challenges for medical publishing
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Cited by
Sorted by citations
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146
Large language models in patient education: a scoping review of applications in medicine
Aydin, S., Karabacak, M., Vlachos, V., & Margetis, K. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1477898 · Cited by 318
Samala, A. D., Rawas, S., Wang, T., Reed, J. M., Kim, J., Howard, N.-J., & Ertz, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12936-0 · Cited by 229
Lum, Z. C. (2023) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000002704 · Cited by 155
Will, J., Gupta, M., Zaretsky, J., Dowlath, A., Testa, P. A., & Feldman, J. (2025) · Journal of Medical Internet Research · doi:10.2196/69955 · Cited by 139
Metadata
- Authors
- Kirchner, G. J., Kim, R. Y., Weddle, J. B., & Bible, J. E.
- Affiliations
- Penn State Milton S. Hershey Medical Center (US)
- Venue
- Clinical Orthopaedics & Related Research
- Published
- 2023-04-28
- Volume
- 481
- Issue
- 11
- Pages
- 2260-2267
- ISSN
- 0009-921X (ISSN-L)
- 1528-1132
- PMC
- PMC10566892
- OpenAlex
- W4367307887
- Cited by
- 208
- References
- 27
- Citations by year
- 2026: 71 · 2025: 74 · 2024: 53 · 2023: 10
- FWCI
- 72.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Clinical Orthopaedics and Related Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Linguistics and Language › Text Readability and Simplification
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- readability, patient education materials, artificial intelligence, AI-generated text, orthopedic surgery, plain language, Flesch-Kincaid Grade Level, Flesch Reading Ease, total knee arthroplasty, total hip arthroplasty, lumbar disc herniation, scoliosis, spinal stenosis, online health information, factual accuracy, conversion time
- MeSH terms
- Artificial Intelligence, Constriction, Pathologic, Humans, Patient Education as Topic, Scoliosis, United States, Internet, Comprehension, Health Literacy
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