ArticleJMIR public health and surveillance2024
Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.
Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Comparing five generative AI chatbots' answers to LLM-generated clinical questions with medical information scientists' evidence summaries.Journal of the Medical Library Association : JMLA · 2026Article
- Scope and Prospects of Social Media for Patient Education and Engagement in Medical Practice.Cureus · 2026Review
- Artificial Intelligence in Public Health Education: A Scoping Review of Workforce Competency Development.Health science reports · 2026Article
- Comparative evaluation of artificial intelligence platforms and drug interaction screening databases using real-world patient data.Exploratory research in clinical and social pharmacy · 2025Article
- Article
- Comparing Five Generative AI Chatbots' Answers to LLM-Generated Clinical Questions with Medical Information Scientists' Evidence Summaries.medRxiv : the preprint server for health sciences · 2025Article
- Viewpoint on the Intersection Among Health Information, Misinformation, and Generative AI Technologies.JMIR infodemiology · 2025Article
- Building an Ethical Framework for European Pharmacists in e-Pharmacies.Journal of medical Internet research · 2025Article
- Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study.Journal of medical Internet research · 2025Article
- Multifactor Quality and Safety Analysis of Semaglutide Products Sold by Online Sellers Without a Prescription: Market Surveillance, Content Analysis, and Product Purchase Evaluation Study.Journal of medical Internet research · 2024Article
- Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study.JMIR infodemiology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThe 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.
methodsWe 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.
resultsOf 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%).
conclusionsWhile 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.