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  • Title: Assessment of Artificial Intelligence Chatbot Responses to Top Searched Queries About Cancer.
    Author: Pan A, Musheyev D, Bockelman D, Loeb S, Kabarriti AE.
    Journal: JAMA Oncol; 2023 Oct 01; 9(10):1437-1440. PubMed ID: 37615960.
    Abstract:
    IMPORTANCE: Consumers are increasingly using artificial intelligence (AI) chatbots as a source of information. However, the quality of the cancer information generated by these chatbots has not yet been evaluated using validated instruments. OBJECTIVE: To characterize the quality of information and presence of misinformation about skin, lung, breast, colorectal, and prostate cancers generated by 4 AI chatbots. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study assessed AI chatbots' text responses to the 5 most commonly searched queries related to the 5 most common cancers using validated instruments. Search data were extracted from the publicly available Google Trends platform and identical prompts were used to generate responses from 4 AI chatbots: ChatGPT version 3.5 (OpenAI), Perplexity (Perplexity.AI), Chatsonic (Writesonic), and Bing AI (Microsoft). EXPOSURES: Google Trends' top 5 search queries related to skin, lung, breast, colorectal, and prostate cancer from January 1, 2021, to January 1, 2023, were input into 4 AI chatbots. MAIN OUTCOMES AND MEASURES: The primary outcomes were the quality of consumer health information based on the validated DISCERN instrument (scores from 1 [low] to 5 [high] for quality of information) and the understandability and actionability of this information based on the understandability and actionability domains of the Patient Education Materials Assessment Tool (PEMAT) (scores of 0%-100%, with higher scores indicating a higher level of understandability and actionability). Secondary outcomes included misinformation scored using a 5-item Likert scale (scores from 1 [no misinformation] to 5 [high misinformation]) and readability assessed using the Flesch-Kincaid Grade Level readability score. RESULTS: The analysis included 100 responses from 4 chatbots about the 5 most common search queries for skin, lung, breast, colorectal, and prostate cancer. The quality of text responses generated by the 4 AI chatbots was good (median [range] DISCERN score, 5 [2-5]) and no misinformation was identified. Understandability was moderate (median [range] PEMAT Understandability score, 66.7% [33.3%-90.1%]), and actionability was poor (median [range] PEMAT Actionability score, 20.0% [0%-40.0%]). The responses were written at the college level based on the Flesch-Kincaid Grade Level score. CONCLUSIONS AND RELEVANCE: Findings of this cross-sectional study suggest that AI chatbots generally produce accurate information for the top cancer-related search queries, but the responses are not readily actionable and are written at a college reading level. These limitations suggest that AI chatbots should be used supplementarily and not as a primary source for medical information.
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