ArticleBMJ quality & safety2025
Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients.
Article in BMJ quality & safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Artificial intelligence-powered chatbots for oral anticancer drug patient information: an assessment of quality.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Counterfactual harm: a counter-argument.American journal of epidemiology · 2026Article
- Reliability and readability of AI chatbot responses to patient questions about robot-assisted radical cystectomy.Journal of robotic surgery · 2026Article
- Who bears liability when AI gives bad prescribing advice.NPJ digital medicine · 2026Article
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- Measuring eHealth Literacy in the European Economic Area, Switzerland, and the United Kingdom: Scoping Review.Journal of medical Internet research · 2026Article
- Are AI chatbots ready for chikungunya public education? Evidence on validity, reliability, and readability.BMC public health · 2026Article
- Exploring Nurses' Perspectives on the Use of Artificial Intelligence Chatbots for Mental Health Support: A Cross-Sectional Study in Greece.Nursing reports (Pavia, Italy) · 2026Article
- Multidisciplinary expert evaluation of large language models on questions regarding bariatric surgery: a comparative analysis of ERNIE Bot 4.0, ChatGPT-4, Claude 3 Opus, and Gemini Pro.Scientific reports · 2026Article
- Artificial Intelligence for Medicines Information: Scoping Review of Clinical Applications and Digital Health Inequalities.Journal of medical Internet research · 2026Article
- Examining Artificial Intelligence Chatbots' Responses in Providing Human Papillomavirus Vaccine Information for Young Adults: Qualitative Content Analysis.JMIR public health and surveillance · 2026Article
- Comparison of ChatGPT and Google in addressing patients' questions on robot-assisted total hip arthroplasty.Joint diseases and related surgery · 2026Article
- Lung Health and Respiratory Syncytial Virus: Podcast of a Patient-Physician Discussion Based on Insights from a Patient Advisory Board Meeting.Infectious diseases and therapy · 2025Article
- Article
- Evaluating a customised large language model (DELSTAR) and its ability to address medication-related questions associated with delirium: a quantitative exploratory study.International journal of clinical pharmacy · 2025Article
- Can large language models detect drug-drug interactions leading to adverse drug reactions?Therapeutic advances in drug safety · 2025Article
- Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Knowledge and attitudes towards artificial intelligence use in healthcare among the general public in Jordan.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSearch engines often serve as a primary resource for patients to obtain drug information. However, the search engine market is rapidly changing due to the introduction of artificial intelligence (AI)-powered chatbots. The consequences for medication safety when patients interact with chatbots remain largely unexplored.
objectiveTo explore the quality and potential safety concerns of answers provided by an AI-powered chatbot integrated within a search engine. METHODOLOGY: Bing copilot was queried on 10 frequently asked patient questions regarding the 50 most prescribed drugs in the US outpatient market. Patient questions covered drug indications, mechanisms of action, instructions for use, adverse drug reactions and contraindications. Readability of chatbot answers was assessed using the Flesch Reading Ease Score. Completeness and accuracy were evaluated based on corresponding patient drug information in the pharmaceutical encyclopaedia drugs.com. On a preselected subset of inaccurate chatbot answers, healthcare professionals evaluated likelihood and extent of possible harm if patients follow the chatbot's given recommendations.
resultsOf 500 generated chatbot answers, overall readability implied that responses were difficult to read according to the Flesch Reading Ease Score. Overall median completeness and accuracy of chatbot answers were 100.0% (IQR 50.0-100.0%) and 100.0% (IQR 88.1-100.0%), respectively. Of the subset of 20 chatbot answers, experts found 66% (95% CI 50% to 85%) to be potentially harmful. 42% (95% CI 25% to 60%) of these 20 chatbot answers were found to potentially cause moderate to mild harm, and 22% (95% CI 10% to 40%) to cause severe harm or even death if patients follow the chatbot's advice.
conclusionsAI-powered chatbots are capable of providing overall complete and accurate patient drug information. Yet, experts deemed a considerable number of answers incorrect or potentially harmful. Furthermore, complexity of chatbot answers may limit patient understanding. Hence, healthcare professionals should be cautious in recommending AI-powered search engines until more precise and reliable alternatives are available.
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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 OpenQuestion graph.