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Archives of Osteoporosis

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01.12.2024 | Original Article

Artificial intelligence insights into osteoporosis: assessing ChatGPT’s information quality and readability

verfasst von: Yakup Erden, Mustafa Hüseyin Temel, Fatih Bağcıer

Erschienen in: Archives of Osteoporosis | Ausgabe 1/2024

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Abstract

Summary

Accessible, accurate information, and readability play crucial role in empowering individuals managing osteoporosis. This study showed that the responses generated by ChatGPT regarding osteoporosis had serious problems with quality and were at a level of complexity that that necessitates an educational background of approximately 17 years.

Purpose

The use of artificial intelligence (AI) applications as a source of information in the field of health is increasing. Readable and accurate information plays a critical role in empowering patients to make decisions about their disease. The aim was to examine the quality and readability of responses provided by ChatGPT, an AI chatbot, to commonly asked questions regarding osteoporosis, representing a major public health problem.

Methods

“Osteoporosis,” “female osteoporosis,” and “male osteoporosis” were identified by using Google trends for the 25 most frequently searched keywords on Google. A selected set of 38 keywords was sequentially inputted into the chat interface of the ChatGPT. The responses were evaluated with tools of the Ensuring Quality Information for Patients (EQIP), the Flesch-Kincaid Grade Level (FKGL), and the Flesch-Kincaid Reading Ease (FKRE).

Results

The EQIP score of the texts ranged from a minimum of 36.36 to a maximum of 61.76 with a mean value of 48.71 as having “serious problems with quality.” The FKRE scores spanned from 13.71 to 56.06 with a mean value of 28.71 and the FKGL varied between 8.48 and 17.63, with a mean value of 13.25. There were no statistically significant correlations between the EQIP score and the FKGL or FKRE scores.

Conclusions

Although ChatGPT is easily accessible for patients to obtain information about osteoporosis, its current quality and readability fall short of meeting comprehensive healthcare standards.

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International Osteoporosis Foundation (IOF). Epidemiology. https://www.osteoporosis.foundation/health-professionals/about-osteoporosis/epidemiology . IOF. Accessed 20 Nov 2023

NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy (2001) Osteoporosis prevention, diagnosis, and therapy. JAMA 285(6):785-795. https://doi.org/10.1001/jama.285.6.785

Genant HK, Cooper C, Poor G et al (1999) Interim report and recommendations of the World Health Organization Task-Force for Osteoporosis. Osteoporos Int 10(4):259–264. https://doi.org/10.1007/s001980050224CrossRefPubMed

Bartl C, Bartl P (2019) Epidemiology of osteoporotic fractures. In: Bartl R, Bartl C (ed) The osteoporosis manual. Springer Nature, Switzerland, 231–32. doi:https://doi.org/10.1007/978-3-030-00731-7_28

Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69S:36–40. https://doi.org/10.1016/j.metabol.2017.01.011CrossRef

Marr B (2018) How is AI used in healthcare—5 powerful real-world examples that show the latest advances. Forbes. https://www.forbes.com/sites/bernardmarr/2018/07/27/how-is-ai-used-in-healthcare-5-powerful-real-world-examples-that-show-the-latest-advances. Accessed 15 Nov 2023

Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ (2023) Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectr 7(2):pkad010. https://doi.org/10.1093/jncics/pkad010CrossRefPubMedPubMedCentral

Jovanović M, Baez M, Casati F (2020) Chatbots as conversational healthcare services. IEEE Internet Comput 25(3):44–51. https://doi.org/10.1109/MIC.2020.3037151CrossRef

OpenAI. CHATGPT: optimizing language models for dialogue. OpenAI. https://openai.com/blog/chatg pt/. Accessed 20 Nowember 2023

10.

Xue VW, Lei P, Cho WC (2023) The potential impact of ChatGPT in clinical and translational medicine. Clin Transl Med 13(3):e1216. https://doi.org/10.1002/ctm2.1216CrossRefPubMedPubMedCentral

11.

Crawford-Manning F, Greenall C, Hawarden A et al (2021) Evaluation of quality and readability of online patient information on osteoporosis and osteoporosis drug treatment and recommendations for improvement. Osteoporos Int 32:1567–1584. https://doi.org/10.1007/s00198-020-05800-7CrossRefPubMedPubMedCentral

12.

Yurdakul OV, Kilicoglu MS, Bagcier F (2021) Evaluating the reliability and readability of online information on osteoporosis. Arch Endocrinol Metab 65(1):85–92. https://doi.org/10.20945/2359-3997000000311CrossRefPubMed

13.

Moult B, Franck LS, Brady H (2004) Ensuring quality information for patients: development and preliminary validation of a new instrument to improve the quality of written health care information. Health Expect 7(2):165–175. https://doi.org/10.1111/j.1369-7625.2004.00273.xCrossRefPubMedPubMedCentral

14.

Boles CD, Liu Y, November-Rider D (2016) Readability levels of dental patient education brochures. Am Dental Hygienists’ Assoc 90(1):28–34

15.

Morales-Torres J, Gutiérrez-Ureña S (2004) Osteoporosis Committee of Pan-American League of Associations for Rheumatology The burden of osteoporosis in Latin America. Osteoporos Int 15(8):625–632. https://doi.org/10.1007/s00198-004-1596-3CrossRefPubMed

16.

Aziziyeh R, Amin M, Habib M et al (2019) The burden of osteoporosis in four Latin American countries: Brazil, Mexico, Colombia, and Argentina. J Med Econ 22(7):638–644. https://doi.org/10.1080/13696998.2019.1590843CrossRefPubMed

17.

Fareed N, Swoboda CM, Jonnalagadda P, Walker DM, Huerta TR (2021) Differences between races in health information seeking and trust over time: evidence from a cross-sectional, pooled analyses of HINTS data. Am J Health Promot 35(1):84–92. https://doi.org/10.1177/0890117120934609CrossRefPubMed

18.

Lohana CK, Samir N (2016) Risk management of osteoporosis in postmenopausal women; a study of women in a teaching hospital. Global J Health Sci 8(11):55505. https://doi.org/10.5539/gjhs.v8n11p36CrossRef

19.

Reginster JY, Sarlet N, Lecart MP (2005) Fractures in osteoporosis: the challenge for the new millennium. Osteoporos Int 16(Suppl 1):S1–S3. https://doi.org/10.1007/s00198-004-1752-9CrossRefPubMed

20.

Fontenot HB, Harris AL (2014) Pharmacologic management of osteoporosis. J Obstet Gynecol Neonatal Nurs 43(2):236-E21. https://doi.org/10.1111/1552-6909.12285CrossRefPubMed

21.

Caraccio DM, Jones MS (2021) Principles of diagnosis and treatment of osteoporosis. In: Esther RJ (ed) Clinical Foundations of Musculoskeletal Medicine. Springer Nature, Switzerland, 77–93. https://doi.org/10.1007/978-3-030-42894-5_7

22.

Agrawal NK, Sharma B (2023) Prevalence of osteoporosis in otherwise healthy Indian males aged 50 years and above. Arch Osteoporos 8:116. https://doi.org/10.1007/s11657-012-0116-xCrossRef

23.

Rao SS, Budhwar N, Ashfaque A (2010) Osteoporosis in men. Am Fam Physician 82(5):503–508PubMed

24.

Richardson JE, Reid MC (2013) The promises and pitfalls of leveraging mobile health technology for pain care. Pain Med 14(11):1621–1626. https://doi.org/10.1111/pme.12206CrossRefPubMed

25.

Temel MH, Batıbay S, Bağcıer F (2023) Quality and readability of online information on cerebral palsy. J Consum Health Internet 27(3):266–281. https://doi.org/10.1080/15398285.2023.2235531CrossRef

26.

Khan B, Fatima H, Qureshi A, et al (2023) Drawbacks of artificial intelligence and their potential solutions in the healthcare sector [published online ahead of print, 2023 Feb 8]. Biomed Mater Devices 1–8. https://doi.org/10.1007/s44174-023-00063-2

27.

Eysenbach G (2002) Infodemiology: the epidemiology of (mis)information. Am J Med 113(9):763–765. https://doi.org/10.1016/s0002-9343(02)01473-0CrossRefPubMed

28.

Millenson ML, Baldwin JL, Zipperer L, Singh H (2018) Beyond Dr Google: the evidence on consumer-facing digital tools for diagnosis. Diagnosis (Berl) 5(3):95–105. https://doi.org/10.1515/dx-2018-0009CrossRefPubMed

29.

Lee JWK, Gotay C, Sawatzky R, Kazanjian A (2019) Interrelatedness of distress among Chinese-speaking patients and family caregivers. Cancer Nurs 42(6):439–447. https://doi.org/10.1097/NCC.000000000000062CrossRefPubMed

Titel: Artificial intelligence insights into osteoporosis: assessing ChatGPT’s information quality and readability
verfasst von: Yakup Erden
Mustafa Hüseyin Temel
Fatih Bağcıer
Publikationsdatum: 01.12.2024
Verlag: Springer London
Erschienen in: Archives of Osteoporosis / Ausgabe 1/2024
Print ISSN: 1862-3522
Elektronische ISSN: 1862-3514
DOI: https://doi.org/10.1007/s11657-024-01376-5

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Artificial intelligence insights into osteoporosis: assessing ChatGPT’s information quality and readability

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