"But can chatbots understand sex?" Attitudes towards artificial intelligence chatbots amongst sexual and reproductive health professionals: An exploratory mixed-methods study.

Tom Nadarzynski, Alexandria Lunt, Nicky Knights, Jake Bayley, Carrie Llewellyn
Author Information
  1. Tom Nadarzynski: University of Westminster, London, UK. ORCID
  2. Alexandria Lunt: Brighton and Sussex Medical School, University of Sussex, Brighton.
  3. Nicky Knights: University of Westminster, London, UK.
  4. Jake Bayley: Barts NHS Trust, London, UK.
  5. Carrie Llewellyn: Brighton and Sussex Medical School, University of Sussex, Brighton. ORCID

Abstract

BACKGROUND: Artificial Intelligence (AI)-enabled chatbots can offer anonymous education about sexual and reproductive health (SRH). Understanding chatbot acceptability and feasibility allows the identification of barriers to the design and implementation.
METHODS: In 2020, we conducted an online survey and qualitative interviews with SRH professionals recruited online to explore the views on AI, automation and chatbots. Qualitative data were analysed thematically.
RESULTS: Amongst 150 respondents (48% specialist doctor/consultant), only 22% perceived chatbots as effective and 24% saw them as ineffective for SRH advice [Mean = 2.91, SD = 0.98, range: 1-5]. Overall, there were mixed attitudes towards SRH chatbots [Mean = 4.03, SD = 0.87, range: 1-7]. Chatbots were most acceptable for appointment booking, general sexual health advice and signposting, but not acceptable for safeguarding, virtual diagnosis, and emotional support. Three themes were identified: "", "", and "".
CONCLUSIONS: Half of SRH professionals were hesitant about the use of chatbots in SRH services, attributed to concerns about patient safety, and lack of familiarity with this technology. Future studies should explore the role of AI chatbots as supplementary tools for SRH promotion. Chatbot designers need to address the concerns of health professionals to increase acceptability and engagement with AI-enabled services.

Keywords

References

  1. J Med Internet Res. 2020 May 14;22(5):e17620 [PMID: 32406857]
  2. Digit Health. 2019 Aug 21;5:2055207619871808 [PMID: 31467682]
  3. Sex Health. 2021 Nov;18(5):385-393 [PMID: 34782055]
  4. Sex Transm Infect. 2010 Aug;86(4):310-4 [PMID: 20551234]
  5. Curr HIV/AIDS Rep. 2020 Jun;17(3):171-179 [PMID: 32347446]
  6. Psychol Mark. 2021 Dec;38(12):2377-2392 [PMID: 34539051]
  7. AIDS Patient Care STDS. 2021 Jan;35(1):5-8 [PMID: 33400588]
  8. J Family Med Prim Care. 2019 Jul;8(7):2328-2331 [PMID: 31463251]
  9. Lancet Digit Health. 2021 Aug;3(8):e467-e468 [PMID: 34325852]
  10. Sex Health. 2022 Oct;19(5):391-405 [PMID: 35863761]
  11. BMJ Sex Reprod Health. 2020 Jul;46(3):210-217 [PMID: 31964779]
  12. Stat Med. 2020 Oct 15;39(23):3059-3073 [PMID: 32578905]
  13. Digit Health. 2021 Dec 08;7:20552076211063012 [PMID: 34917391]
  14. J Med Internet Res. 2020 Aug 7;22(8):e17158 [PMID: 32763886]
  15. J Med Internet Res. 2020 Jul 13;22(7):e16021 [PMID: 32673216]
  16. JMIR Cancer. 2021 Nov 29;7(4):e27850 [PMID: 34847056]
  17. Int J Behav Nutr Phys Act. 2021 Dec 11;18(1):160 [PMID: 34895247]
  18. J Clin Med. 2022 Mar 25;11(7): [PMID: 35407428]
  19. Am J Obstet Gynecol. 2023 Jun;228(6):696-705 [PMID: 36924907]
  20. Children (Basel). 2021 Oct 26;8(11): [PMID: 34828681]
  21. JAMA Netw Open. 2018 Nov 2;1(7):e185293 [PMID: 30646397]
  22. J Med Internet Res. 2019 Apr 05;21(4):e12887 [PMID: 30950796]
  23. Int J STD AIDS. 2021 Oct;32(12):1138-1148 [PMID: 34106016]
  24. BMC Med Res Methodol. 2021 Jul 31;21(1):159 [PMID: 34332540]

MeSH Term

Humans
Reproductive Health
Sexual Health
Artificial Intelligence
Sexual Behavior
Reproductive Health Services

Word Cloud

Created with Highcharts 10.0.0chatbotsSRHhealth=AIsexualprofessionals""canreproductiveacceptabilityonlineexploreadvice[MeanSD0range:towardsacceptableservicesconcernsBACKGROUND:ArtificialIntelligence-enabledofferanonymouseducationUnderstandingchatbotfeasibilityallowsidentificationbarriersdesignimplementationMETHODS:2020conductedsurveyqualitativeinterviewsrecruitedviewsautomationQualitativedataanalysedthematicallyRESULTS:Amongst150respondents48%specialistdoctor/consultant22%perceivedeffective24%sawineffective291981-5]Overallmixedattitudes403871-7]ChatbotsappointmentbookinggeneralsignpostingsafeguardingvirtualdiagnosisemotionalsupportThreethemesidentified:CONCLUSIONS:HalfhesitantuseattributedpatientsafetylackfamiliaritytechnologyFuturestudiesrolesupplementarytoolspromotionChatbotdesignersneedaddressincreaseengagementAI-enabled"Butunderstandsex?"Attitudesartificialintelligenceamongstprofessionals:exploratorymixed-methodsstudyEuropelocationprevention

Similar Articles

Cited By (5)