Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.

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  • Additional Information
    • Source:
      Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101669345 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-2960 (Electronic) Linking ISSN: 23692960 NLM ISO Abbreviation: JMIR Public Health Surveill Subsets: MEDLINE
    • Publication Information:
      Original Publication: Toronto : JMIR Publications, [2015]-
    • Subject Terms:
      Artificial Intelligence* ; Controlled Substances*; Humans ; Public Health ; Search Engine ; Databases, Factual
    • Abstract:
      Background: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors.
      Objective: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations.
      Methods: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases.
      Results: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%).
      Conclusions: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.
      (©Amir Reza Ashraf, Tim Ken Mackey, András Fittler. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 21.03.2024.)
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    • Contributed Indexing:
      Keywords: Bing; Google; artificial intelligence; comparative assessment; consumer; consumers; controlled substance; controlled substances; customer; customers; drug; drugs; generative; generative artificial intelligence; illegal; information seeking; medication; medications; online pharmacies; patient safety; pharmaceutic; pharmaceutical; pharmaceuticals; pharmaceutics; pharmacies; pharmacy; recommendation; recommendations; retrieval; risk; risks; safety; search; search engine; search engines; searches; searching; substance abuse; substance use; vendor; vendors; website; websites
    • Accession Number:
      0 (Controlled Substances)
    • Publication Date:
      Date Created: 20240321 Date Completed: 20240322 Latest Revision: 20240407
    • Publication Date:
      20240407
    • Accession Number:
      PMC10995787
    • Accession Number:
      10.2196/53086
    • Accession Number:
      38512343