Assessment of Quality and Readability of Information Provided by ChatGPT in Relation to Anterior Cruciate Ligament Injury.

ACL reconstruction surgery (ACL-R) ChatGPT DISCERN criteria anterior cruciate ligament (ACL) artificial intelligence (AI) health literacy natural language processing orthopaedic injuries patient education materials (PEMS) readability

Journal

Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269

Informations de publication

Date de publication:
18 Jan 2024
Historique:
received: 03 11 2023
revised: 17 12 2023
accepted: 20 12 2023
medline: 22 1 2024
pubmed: 22 1 2024
entrez: 22 1 2024
Statut: epublish

Résumé

The aim of our study was to evaluate the potential role of Artificial Intelligence tools like ChatGPT in patient education. To do this, we assessed both the quality and readability of information provided by ChatGPT 3.5 and 4 in relation to Anterior Cruciate Ligament (ACL) injury and treatment. ChatGPT 3.5 and 4 were used to answer common patient queries relating to ACL injuries and treatment. The quality of the information was assessed using the DISCERN criteria. Readability was assessed with the use of seven readability formulae: the Flesch-Kincaid Reading Grade Level, the Flesch Reading Ease Score, the Raygor Estimate, the SMOG, the Fry, the FORCAST, and the Gunning Fog. The mean reading grade level (RGL) was compared with the recommended 8th-grade reading level, the mean RGL among adults in America. The perceived quality and mean RGL of answers given by both ChatGPT 3.5 and 4 was also compared. Both ChatGPT 3.5 and 4 yielded DISCERN scores suggesting "good" quality of information, with ChatGPT 4 slightly outperforming 3.5. However, readability levels for both versions significantly exceeded the average 8th-grade reading level for American patients. ChatGPT 3.5 had a mean RGL of 18.08, while the mean RGL of ChatGPT 4 was 17.9, exceeding the average American reading grade level by 10.08 grade levels and 9.09 grade levels, respectively. While ChatGPT can provide both reliable and good quality information on ACL injuries and treatment options, the readability of the content may limit its utility. Additionally, the consistent lack of source citation represents a significant area of concern for patients and clinicians alike. If AI is to play a role in patient education, it must reliably produce information which is accurate, easily comprehensible, and clearly sourced.

Identifiants

pubmed: 38248805
pii: jpm14010104
doi: 10.3390/jpm14010104
pii:
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Stephen Fahy (S)

Centrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.

Stephan Oehme (S)

Centrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.

Danko Milinkovic (D)

Centrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.

Tobias Jung (T)

Centrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.

Benjamin Bartek (B)

Centrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin, 10117 Berlin, Germany.

Classifications MeSH