Evaluating the effectiveness of artificial intelligence‐based tools in detecting and understanding sleep health misinformation: Comparative analysis using Google Bard and OpenAI ChatGPT‐4
Open accessAbstract
Summary: This study evaluates the performance of two major artificial intelligence‐based tools (ChatGPT‐4 and Google Bard) in debunking sleep‐related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5‐point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT‐4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (−0.42) and kurtosis (−0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT‐4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and −0.07, respectively, indicating a more normal distribution compared with ChatGPT‐4. The inter‐rater agreement between Google Bard and sleep experts had an intra‐class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text‐mining analysis revealed Google Bard's focus on practical advice, while ChatGPT‐4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th‐grade level material, versus ChatGPT‐4's 12th‐grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence‐generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The level of alignment varied based on whether claims were assessed for factual falseness or their public health significance.
“However, the level of alignment varied based on whether claims were assessed for factual falseness or their public health significance”
From Generative AI and misinformation: a scoping review of the role of generative AI in the generation, detection, mitigation, and impact of misinformation (2026) · cites this paper as Garbarino and Bragazzi 2024
There was a moderately strong agreement between ChatGPT-4.0 and Google Bard evaluations of sleep-related misinformation and expert assessments.
“The study found a moderately strong agreement between ChatGPT-4.0 and Google Bard evaluations of sleep-related misinformation and expert assessments.”
From Generative AI and misinformation: a scoping review of the role of generative AI in the generation, detection, mitigation, and impact of misinformation (2026) · cites this paper as Garbarino and Bragazzi 2024
Google Bard slightly outperformed ChatGPT-4.0 in accurately identifying false claims.
“While Google Bard slightly outperformed ChatGPT-4.0 in accurately identifying false claims”
From Generative AI and misinformation: a scoping review of the role of generative AI in the generation, detection, mitigation, and impact of misinformation (2026) · cites this paper as Garbarino and Bragazzi 2024
ChatGPT-4.0 demonstrated stronger alignment with expert assessments, particularly in evaluating both the factual falseness and the public health significance of the myths.
“ChatGPT-4.0 demonstrated stronger alignment with expert assessments, particularly in evaluating both the factual falseness and the public health significance of the myths.”
From Generative AI and misinformation: a scoping review of the role of generative AI in the generation, detection, mitigation, and impact of misinformation (2026) · cites this paper as Garbarino and Bragazzi 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Dumais, S. (2004) · Annual Review of Information Science and Technology · doi:10.1002/aris.1440380105 · Cited by 1,123 · see citing works
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01595-0 · Cited by 928 · see citing works
Intraclass correlation – A discussion and demonstration of basic features
Liljequist, D., Elfving, B., & Roaldsen, K. S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219854 · Cited by 910 · see citing works
Sleep is essential to health: an American Academy of Sleep Medicine position statement
Ramar, K., Malhotra, R. K., Carden, K. A., Martin, J. L., Abbasi-Feinberg, F., Aurora, R. N., Kapur, V. K., Olson, E. J., Rosen, C. L., Rowley, J. A., et al. (2021) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.9476 · Cited by 648 · see citing works
Athaluri, S. A., Manthena, S. V., Kesapragada, V. S. R. K. M., Yarlagadda, V., Dave, T., & Duddumpudi, R. T. S. (2023) · Cureus · doi:10.7759/cureus.37432 · Cited by 403 · see citing works
The need to promote sleep health in public health agendas across the globe
Lim, D. C., Najafi, A., Afifi, L., Bassetti, C. L., Buysse, D. J., Han, F., Högl, B., Melaku, Y. A., Morin, C. M., Pack, A. I., et al. (2023) · The Lancet Public Health · doi:10.1016/s2468-2667(23)00182-2 · Cited by 373 · see citing works
The future of sleep health: a data-driven revolution in sleep science and medicine
Perez-Pozuelo, I., Zhai, B., Palotti, J., Mall, R., Aupetit, M., Garcia-Gomez, J. M., Taheri, S., Guan, Y., & Fernandez-Luque, L. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0244-4 · Cited by 311 · see citing works
Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581318 · Cited by 288 · see citing works
AI pitfalls and what not to do: mitigating bias in AI
Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20230023 · Cited by 277 · see citing works
ChatGPT: these are not hallucinations – they’re fabrications and falsifications
Emsley, R. (2023) · Schizophrenia · doi:10.1038/s41537-023-00379-4 · Cited by 258 · see citing works
Sleep, Health and Wellness at Work: A Scoping Review
Magnavita, N., & Garbarino, S. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14111347 · Cited by 247 · see citing works
Self-citationArtificial intelligence and increasing misinformation
Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E., & Bauer, M. (2024) · The British Journal of Psychiatry · doi:10.1192/bjp.2023.136 · Cited by 208 · see citing works
ChatGPT and Bard exhibit spontaneous citation fabrication during psychiatry literature search
McGowan, A., Gui, Y., Dobbs, M., Shuster, S., Cotter, M., Selloni, A., Goodman, M., Srivastava, A., Cecchi, G. A., & Corcoran, C. M. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115334 · Cited by 140 · see citing works
Hallucinations in ChatGPT: A Cautionary Tale for Biomedical Researchers
Goddard, J. (2023) · The American Journal of Medicine · doi:10.1016/j.amjmed.2023.06.012 · Cited by 89 · see citing works
Evaluating ChatGPT responses on obstructive sleep apnea for patient education
Campbell, D. J., Estephan, L. E., Mastrolonardo, E. V., Amin, D. R., Huntley, C. T., & Boon, M. S. (2023) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.10728 · Cited by 82 · see citing works
Large Language Models: A Guide for Radiologists
Kim, S., Lee, C., & Kim, S. (2024) · Korean Journal of Radiology · doi:10.3348/kjr.2023.0997 · Cited by 79 · see citing works
Readability formulas: Useful or useless?
McClure, G. M. (1987) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.1987.6449109 · Cited by 73 · see citing works
Robbins, R., Grandner, M. A., Buxton, O. M., Hale, L. E., Buysse, D. J., Knutson, K. L., Patel, S. R., Troxel, W., Youngstedt, S. D., Czeisler, C. A., et al. (2019) · Sleep Health · doi:10.1016/j.sleh.2019.02.002 · Cited by 61 · see citing works
Tsai, H.-J., Yang, A. C., Zhu, J.-D., Hsu, Y.-Y., Hsu, T.-F., & Tsai, S.-J. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12030481 · Cited by 45 · see citing works
Chat GPT for the management of obstructive sleep apnea: do we have a polar star?
Mira, F. A., Favier, V., Nunes, H. D. S. S., de Castro, J. V., Carsuzaa, F., Meccariello, G., Vicini, C., Vito, A. D., Lechien, J. R., Chiesa-Estomba, C. M., et al. (2023) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-023-08270-9 · Cited by 43 · see citing works
Cheong, R. C. T., Pang, K. P., Unadkat, S. N., Mcneillis, V., Williamson, A., Joseph, J., Randhawa, P. S., Andrews, P. J., & Paleri, V. (2023) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-023-08381-3 · Cited by 43 · see citing works
SleepOMICS: How Big Data Can Revolutionize Sleep Science
Bragazzi, N. L., Guglielmi, O., & Garbarino, S. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16020291 · Cited by 40 · see citing works
Self-citationRedline, S. S., & Purcell, S. (2021) · SLEEP · doi:10.1093/sleep/zsab107 · Cited by 25 · see citing works
Bilal, M., Jamil, Y., Rana, D., & Shah, H. H. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03298-8 · Cited by 25 · see citing works
Bragazzi, N. L., & Garbarino, S. (2024) · JMIR Formative Research · doi:10.2196/55762 · Cited by 19 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Large language models in patient education: a scoping review of applications in medicine
Aydin, S., Karabacak, M., Vlachos, V., & Margetis, K. D. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1477898 · Cited by 330 · see citing works
Toward Clinical Generative AI: Conceptual Framework
Bragazzi, N. L., & Garbarino, S. (2024) · JMIR AI · doi:10.2196/55957 · Cited by 64 · see citing works
Self-citationRevolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine
Garbarino, S., & Bragazzi, N. L. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14060598 · Cited by 49 · see citing works
Self-citationPark, S., & Nan, X. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02620-3 · Cited by 58 · see citing works
“A total of six studies (Deiana et al. 2023; Garbarino and Bragazzi 2024; Kumar et al. 2024, Study 1; Santangeli et al. 2024; Senekal and Brokensha 2023; Węcel et al. 2023) investigated the ability of LLMs to detect misinformation across diverse topics, including health and conspiracy theories.”
Olszewski, R., Watros, K., Mańczak, M., Owoc, J., Jeziorski, K., & Brzeziński, J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105562 · Cited by 44 · see citing works
Jahrami, H., Husain, W., Trabelsi, K., Penzel, T., Hirshkowitz, M., Razjouyan, J., BaHammam, A. S., & Sharafkhaneh, A. (2025) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2025.102212 · Cited by 16 · see citing works
Sun, Z., Yim, W.-W., Uzuner, Ö., Xia, F., & Yetişgen, M. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104866 · Cited by 18 · see citing works
Advancing Patient Education in Idiopathic Intracranial Hypertension
Dihan, Q. A., Brown, A. D., Zaldivar, A. T., Chauhan, M. Z., Eleiwa, T. K., Hassan, A. K., Solyman, O. M., Gise, R. A., Phillips, P. H., Sallam, A. B., et al. (2024) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000200366 · Cited by 10 · see citing works
ChatGPT as an Information Source for Patients with Migraines: A Qualitative Case Study
Schütz, P., Lob, S., Chahed, H., Dathe, L., Löwer, M., Reiß, H., Weigel, A., Albrecht, J., Tokgöz, P., & Dockweiler, C. (2024) · Healthcare · doi:10.3390/healthcare12161594 · Cited by 9 · see citing works
Lauderdale, S. A., Griffin, S. A., Lahman, K. R., Mbaba, E., & Tomlinson, S. (2025) · Personality and Mental Health · doi:10.1002/pmh.70018 · Cited by 9 · see citing works
Health Misinformation Detection: Approaches, Challenges and Opportunities
Feng, X., Luo, J., Yang, Y., Baz, D. E., & Shi, L. (2025) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580251384784 · Cited by 7 · see citing works
Vitale, J. A., McCall, A., Cina, A., Athlete Travel, S. I. G. (., Alonso, J.-M., Bender, A., Biggins, M., Charest, J., Dohi, M., Filip-Stachnik, A., et al. (2025) · Sports Medicine · doi:10.1007/s40279-025-02303-5 · Cited by 4 · see citing works
Bragazzi, N. L., & Garbarino, S. (2024) · JMIR Infodemiology · doi:10.2196/57748 · Cited by 2 · see citing works
Self-citationKomsany, A., Zoubi, O. A., Sebaaly, L., Harrison, G., Soroka, O., Elkefi, S., Scales, D. A., Phillips, E. P., Pinheiro, L. C., Ismail, I., et al. (2026) · JMIR Infodemiology · doi:10.2196/77888
Generative AI and health misinformation: a lifecycle-based communication framework
Nan, X. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1801842
- Authors
- Garbarino, S., & Bragazzi, N. L.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- University of Parma (IT)
- Corresponding author
- Bragazzi, Nicola Luigi
- Venue
- Journal of Sleep Research
- Publisher
- Wiley
- Published
- 2024-04-05
- Volume
- 33
- Issue
- 6
- Pages
- e14210-e14210
- ISSN
- 0962-1105 (ISSN-L)
- 1365-2869
- PMC
- PMC11596984
- OpenAlex
- W4393989759
- Cited by
- 15
- References
- 25
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 7
- FWCI
- 10.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Sleep Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- ChatGPT-4, Google Bard, large language models, misinformation detection, public health education, readability, text mining, expert validation, sleep health promotion
- MeSH terms
- Artificial Intelligence, Communication, Humans, Sleep, Internet
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
