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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Sep 25, 2023
Date Accepted: Jan 4, 2024

The final, peer-reviewed published version of this preprint can be found here:

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

Ashraf AR, Mackey TK, Fittler A

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

JMIR Public Health Surveill 2024;10:e53086

DOI: 10.2196/53086

PMID: 38512343

PMCID: 10995787

Search Engines and Generative AI Integration: Public Health Risks and Recommendations to Safeguard Consumers Online

  • Amir Reza Ashraf; 
  • Tim Ken Mackey; 
  • AndrĂ¡s Fittler

ABSTRACT

Background:

The online pharmacy market is growing exponentially, with legitimate online pharmacies offering advantages such as convenience and accessibility. These factors became particularly important during the COVID-19 pandemic, where consumers increasingly turned to the Internet for health information seeking and purchasing of medications remotely. However, this increased demand has attracted malicious actors into the online pharmacy space, leading to proliferation of illegal online pharmacies that employ deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard, fake, or falsified medicines. Despite warnings from experts and researchers, search engine providers have yet to enforce more stringent controls and illegal online pharmacy operations continue to persist unchecked.

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 engine results and recommendations.

Methods:

We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience 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.com databases.

Results:

Of the 262 websites recommended in the AI generated search results, 47.33% (n/N=124/262) belonged to active online pharmacies, with 31.29% (n/N=82/262) leading to legitimate ones. However, 19.84% (n/N=25/126) of Bing's and 12.5% (n/N=17/136) of Google's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine.

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 online 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. Clinical Trial: Not applicable


 Citation

Please cite as:

Ashraf AR, Mackey TK, Fittler A

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

JMIR Public Health Surveill 2024;10:e53086

DOI: 10.2196/53086

PMID: 38512343

PMCID: 10995787

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