Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Comparing ChatGPT and Bing, in response to the Home Blood Pressure Monitoring (HBPM) knowledge checklist

Hypertension Research volume 47, pages 1401–1409 (2024)Cite this article

Abstract

High blood pressure is one of the major public health problems that is prevalent worldwide. Due to the rapid increase in the number of users of artificial intelligence tools such as ChatGPT and Bing, it is expected that patients will use these tools as a source of information to obtain information about high blood pressure. The purpose of this study is to check the accuracy, completeness, and reproducibility of answers provided by ChatGPT and Bing to the knowledge questionnaire of blood pressure control at home. In this study, ChatGPT and Bing’s responses to the HBPM 10-question knowledge checklist on blood pressure measurement were independently reviewed by three cardiologists. The mean accuracy rating of ChatGPT was 5.96 (SD = 0.17) indicating the responses were highly accurate overall, with the vast majority receiving the top score. The mean accuracy and completeness of ChatGPT were 5.96 (SD = 0.17) and 2.93 (SD = 0.25) and in Bing were 5.31 (SD = 0.67), and 2.13 (SD = 0.53) Respectively. Due to the expansion of artificial intelligence applications, patients can use new tools such as ChatGPT and Bing to search for information and at the same time can trust the information obtained. we found that the answers obtained from ChatGPT are reliable and valuable for patients, while Bing is also considered a powerful tool, it has more limitations than ChatGPT, and the answers should be interpreted with caution.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. WHO. Hypertension 2023. Available from: https://www.who.int/health-topics/hypertension#tab=tab_1.

  2. Lawes CM, Vander Hoorn S, Rodgers A, International Society of H. Global burden of blood-pressure-related disease, 2001. Lancet. 2008;371:1513–8.

    Article  PubMed  Google Scholar 

  3. Pan J, Lei T, Hu B, Li Q. Post-discharge evaluation of medication adherence and knowledge of hypertension among hypertensive stroke patients in northwestern China. Patient Prefer Adherence. 2017;11:1915–22.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Akoko BM, Fon PN, Ngu RC, Ngu KB. Knowledge of hypertension and compliance with therapy among hypertensive patients in the Bamenda Health District of Cameroon: a cross-sectional study. Cardiol Ther. 2017;6:53–67.

    Article  PubMed  Google Scholar 

  5. Abegaz TM, Shehab A, Gebreyohannes EA, Bhagavathula AS, Elnour AA. Nonadherence to antihypertensive drugs: a systematic review and meta-analysis. Medicine. 2017;96:e5641.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sharma S, Bhuvan K, Alrasheedy AA, Kaundinnyayana A, Khanal A. Impact of community pharmacy-based educational intervention on patients with hypertension in Western Nepal. Australas Med J. 2014;7:304–13.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Almas A, Godil SS, Lalani S, Samani ZA, Khan AH. Good knowledge about hypertension is linked to better control of hypertension; a multicentre cross sectional study in Karachi, Pakistan. BMC Res Notes. 2012;5:1–8.

    Article  Google Scholar 

  8. Whelton PK, Carey RM, Aronow WS. ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, Detection, evaluation, and management of high blood pressure in adults: a Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018;7:68–74.

    Google Scholar 

  9. McCormack T, Krause T, O’Flynn N. Management of hypertension in adults in primary care: NICE guideline. Br J Gen Pr. 2012;62:163–4.

    Article  Google Scholar 

  10. Glynn LG, Murphy AW, Smith SM, Schroeder K, Fahey T. Self-monitoring and other non-pharmacological interventions to improve the management of hypertension in primary care: a systematic review. Br J Gen Pr. 2010;60:e476–88.

    Article  Google Scholar 

  11. Tucker KL, Sheppard JP, Stevens R, Bosworth HB, Bove A, Bray EP, et al. Self-monitoring of blood pressure in hypertension: a systematic review and individual patient data meta-analysis. PLoS Med. 2017;14:e1002389.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Fu SN, Dao MC, Wong CKH, Cheung BMY. The association of health literacy with high-quality home blood pressure monitoring for hypertensive patients in outpatient settings. Int J Hypertens. 2020;2020:7502468–15.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Van Dis EA, Bollen J, Zuidema W, van Rooij R, Bockting CL. ChatGPT: five priorities for research. Nature. 2023;614:224–6.

    Article  PubMed  Google Scholar 

  14. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Bohr A, Memarzadeh K, editors. Artificial Intelligence in Healthcare. 2020;25–60. https://doi.org/10.1016/b978-0-12-818438-7.00002-2.

  15. Wen Z, Huang H. The potential for artificial intelligence in healthcare. J Commerc Biotechnol. 2022;27.

  16. Tan P, Xi Y, Chao S, Jiang D, Liu Z, Fan Y, et al. An artificial intelligence-enhanced blood pressure monitor wristband based on piezoelectric nanogenerator. Biosens. 2022;12:234.

    Article  CAS  Google Scholar 

  17. Hare AJ, Chokshi N, Adusumalli S. Novel digital technologies for blood pressure monitoring and hypertension management. Curr Cardiovasc Risk Rep. 2021;15:11.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yeo YH, Samaan JS, Ng WH, Ting PS, Trivedi H, Vipani A, et al. Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma. Clin Mol Hepatol. 2023;29:721–32.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Graham F. Daily briefing: Will ChatGPT kill the essay assignment?. Nature. 2022.https://doi.org/10.1038/d41586-022-04437-2.

  20. O’Connor S. Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Educ Pr. 2023;66:103537.

    Article  Google Scholar 

  21. Lopezosa C. Bing chat: hacia una nueva forma de entender las búsquedas. Anuario ThinkEPI. 2023;17.

  22. Alberts IL, Mercolli L, Pyka T, Prenosil G, Shi K, Rominger A, et al. Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be? Eur J Nucl Med Mol imaging. 2023;50:1549–52.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bing Chat Details. 2023. https://www.bing.com/new.

  24. Fu SN, Dao MC, Wong CK, Cheung BM. Knowledge and practice of home blood pressure monitoring 6 months after the risk and assessment management programme: does health literacy matter? Postgrad Med J. 2022;98:610–6.

    Article  PubMed  Google Scholar 

  25. Samaan JS, Yeo YH, Rajeev N, Hawley L, Abel S, Ng WH, et al. Assessing the accuracy of responses by the language model ChatGPT to questions regarding bariatric surgery. Obes Surg. 2023;33:1–7.

    Google Scholar 

  26. Rahsepar AA, Tavakoli N, Kim GHJ, Hassani C, Abtin F, Bedayat A. How AI responds to common lung cancer questions: ChatGPT vs Google Bard. Radiology. 2023;307:e230922.

    Article  PubMed  Google Scholar 

  27. Butte AJ. Artificial intelligence-from starting pilots to scalable privilege. JAMA Oncol. 2023;9:1341–2.

    Article  PubMed  Google Scholar 

  28. Cai LZ, Shaheen A, Jin A, Fukui R, Yi JS, Yannuzzi N, et al. Performance of generative large language models on ophthalmology board-style questions. Am J Ophthalmol. 2023;254:141–9.

    Article  PubMed  Google Scholar 

  29. Kumari A, Kumari A, Singh A, Singh SK, Juhi A, Dhanvijay AKD, et al. Large language models in hematology case solving: a comparative study of ChatGPT-3.5, Google Bard, and Microsoft Bing. Cureus. 2023;15:e43861.

    PubMed  PubMed Central  Google Scholar 

  30. Pham BD, Kim BG, Nguyen TTH, Hoang VM. Exposure to messages on risk factors for noncommunicable diseases in a rural province of Vietnam. Biomed Res Int. 30;2019:7962947.

  31. Lee YJ, Boden-Albala B, Jia H, Wilcox A, Bakken S. The association between online health information–seeking behaviors and health behaviors among Hispanics in New York City: a community-based cross-sectional study. J Med Internet Res. 2015;17:e261.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ng JY, Jomy J, Vacca A. Evaluation of the quality of online patient information at the intersection of complementary and alternative medicine and hypertension. Clin Hypertens. 2022;28:9.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tahir M, Usman M, Muhammad F, Rehman SU, Khan I, Idrees M, et al. Evaluation of quality and readability of online health information on high blood pressure using DISCERN and Flesch-Kincaid tools. Appl Sci. 2020;10:3214.

    Article  CAS  Google Scholar 

  34. Oloidi A, Nduaguba SO, Obamiro K. Assessment of quality and readability of internet-based health information related to commonly prescribed angiotensin receptor blockers. Pan Afr Med J. 2020;35:70.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Van Bulck L, Moons P. What if your patient switches from Dr. Google to Dr. ChatGPT? A vignette-based survey of the trustworthiness, value, and danger of ChatGPT-generated responses to health questions. Eur J Cardiovasc Nurs. 2023.

Download references

Acknowledgements

In this section, Fasa University of Medical Sciences was thanked for its financial support.

Funding

This study was supported by the Fasa University of Medical Sciences.

Author information

Authors and Affiliations

  1. Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran

    Michaeel Motaghi Niko

  2. Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran

    Zahra Karbasi

  3. Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran

    Maryam Kazemi & Maryam Zahmatkeshan

  4. School of Allied Medical Sciences, Fasa University of Medical Sciences, Fasa, Iran

    Maryam Zahmatkeshan

Authors
  1. Michaeel Motaghi Niko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zahra Karbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maryam Kazemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maryam Zahmatkeshan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MM, ZK wrote the first draft of the manuscript. Authors MM, ZK, MK, MZ performed data collection. MK performed an analysis and extracted the main characteristics. All authors MM, ZK, MK, MZ reviewed and provided critical feedback. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Maryam Zahmatkeshan.

Ethics declarations

Conflict of interest

The authors declare no competing interests. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics statement

This study was extracted from a research supported financially by the Fasa University of Medical Sciences with the ethics code of IR.FUMS.REC.1402.025. URL: https://ethics.research.ac.ir/ProposalCertificateEn.php?id=340398&Print=true&NoPrintHeader=true&NoPrintFooter=true&NoPrintPageBorder=true&LetterPrint=true

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niko, M.M., Karbasi, Z., Kazemi, M. et al. Comparing ChatGPT and Bing, in response to the Home Blood Pressure Monitoring (HBPM) knowledge checklist. Hypertens Res 47, 1401–1409 (2024). https://doi.org/10.1038/s41440-024-01624-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41440-024-01624-8

Keywords

Search

Advanced search

Quick links