Dataset Information

What Does ChatGPT Know About Dementia? A Comparative Analysis of Information Quality.

ABSTRACT: The quality of information about dementia retrieved using ChatGPT is unknown. Content was evaluated for length, readability, and quality using the QUEST, a validated tool, and compared against online material from three North American organizations. Both sources of information avoided conflicts of interest, supported the patient-physician relationship, and used a balanced tone. Official bodies but not ChatGPT referenced identifiable research and pointed to local resources. Users of ChatGPT are likely to encounter accurate but shallow information about dementia. Recommendations are made for information creators and providers who counsel patients around digital health practices.

SUBMITTER: Dosso JA

PROVIDER: S-EPMC10836539 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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What Does ChatGPT Know About Dementia? A Comparative Analysis of Information Quality.

Dosso Jill A JA Kailley Jaya N JN Robillard Julie M JM

Journal of Alzheimer's disease : JAD 20240101 2

The quality of information about dementia retrieved using ChatGPT is unknown. Content was evaluated for length, readability, and quality using the QUEST, a validated tool, and compared against online material from three North American organizations. Both sources of information avoided conflicts of interest, supported the patient-physician relationship, and used a balanced tone. Official bodies but not ChatGPT referenced identifiable research and pointed to local resources. Users of ChatGPT are l ...[more]

PMID: 38143345

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Project description:BackgroundThe widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China's DingXiangYuan to offer remote medical services.ObjectiveThis study evaluates ChatGPT-4's responses against those of professional health care providers in telemedicine, assessing artificial intelligence's capability to support the surge in remote medical consultations and its impact on health care delivery.MethodsWe sourced remote orthopedic consultations from "Doctor DingXiang," with responses from its certified physicians as the control and ChatGPT's responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: "logical reasoning," "internal information," "external information," "guiding function," "therapeutic effect," "medical knowledge popularization education," and "overall satisfaction." We used Fleiss κ to measure agreement among multiple raters.ResultsInitially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT's "internal information" score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; P=.43) and "therapeutic effect" score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; P=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher "logical reasoning" score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; P=.38), "external information" score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; P=.25), and "guiding function" score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; P=.96), although the differences were not statistically significant. Meanwhile, the "medical knowledge popularization education" score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; P<.001), and the difference was statistically significant. In terms of "overall satisfaction," the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; P=.92). According to how Fleiss κ values were interpreted, 6 of the control group's score points were classified as displaying "fair agreement" (P<.001), and 1 was classified as showing "substantial agreement" (P<.001). In the experimental group, 3 points were classified as indicating "fair agreement," while 4 suggested "moderate agreement" (P<.001).ConclusionsChatGPT-4 matches the expertise found in DingXiangYuan forums' paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries.

Project description:BackgroundChatGPT-4 is the latest release of a novel artificial intelligence (AI) chatbot able to answer freely formulated and complex questions. In the near future, ChatGPT could become the new standard for health care professionals and patients to access medical information. However, little is known about the quality of medical information provided by the AI.ObjectiveWe aimed to assess the reliability of medical information provided by ChatGPT.MethodsMedical information provided by ChatGPT-4 on the 5 hepato-pancreatico-biliary (HPB) conditions with the highest global disease burden was measured with the Ensuring Quality Information for Patients (EQIP) tool. The EQIP tool is used to measure the quality of internet-available information and consists of 36 items that are divided into 3 subsections. In addition, 5 guideline recommendations per analyzed condition were rephrased as questions and input to ChatGPT, and agreement between the guidelines and the AI answer was measured by 2 authors independently. All queries were repeated 3 times to measure the internal consistency of ChatGPT.ResultsFive conditions were identified (gallstone disease, pancreatitis, liver cirrhosis, pancreatic cancer, and hepatocellular carcinoma). The median EQIP score across all conditions was 16 (IQR 14.5-18) for the total of 36 items. Divided by subsection, median scores for content, identification, and structure data were 10 (IQR 9.5-12.5), 1 (IQR 1-1), and 4 (IQR 4-5), respectively. Agreement between guideline recommendations and answers provided by ChatGPT was 60% (15/25). Interrater agreement as measured by the Fleiss κ was 0.78 (P<.001), indicating substantial agreement. Internal consistency of the answers provided by ChatGPT was 100%.ConclusionsChatGPT provides medical information of comparable quality to available static internet information. Although currently of limited quality, large language models could become the future standard for patients and health care professionals to gather medical information.

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Project description:BackgroundSentiment analysis is a significant yet difficult task in natural language processing. The linguistic peculiarities of Cantonese, including its high similarity with Standard Chinese, its grammatical and lexical uniqueness, and its colloquialism and multilingualism, make it different from other languages and pose additional challenges to sentiment analysis. Recent advances in models such as ChatGPT offer potential viable solutions.ObjectiveThis study investigated the efficacy of GPT-3.5 and GPT-4 in Cantonese sentiment analysis in the context of web-based counseling and compared their performance with other mainstream methods, including lexicon-based methods and machine learning approaches.MethodsWe analyzed transcripts from a web-based, text-based counseling service in Hong Kong, including a total of 131 individual counseling sessions and 6169 messages between counselors and help-seekers. First, a codebook was developed for human annotation. A simple prompt ("Is the sentiment of this Cantonese text positive, neutral, or negative? Respond with the sentiment label only.") was then given to GPT-3.5 and GPT-4 to label each message's sentiment. GPT-3.5 and GPT-4's performance was compared with a lexicon-based method and 3 state-of-the-art models, including linear regression, support vector machines, and long short-term memory neural networks.ResultsOur findings revealed ChatGPT's remarkable accuracy in sentiment classification, with GPT-3.5 and GPT-4, respectively, achieving 92.1% (5682/6169) and 95.3% (5880/6169) accuracy in identifying positive, neutral, and negative sentiment, thereby outperforming the traditional lexicon-based method, which had an accuracy of 37.2% (2295/6169), and the 3 machine learning models, which had accuracies ranging from 66% (4072/6169) to 70.9% (4374/6169).ConclusionsAmong many text analysis techniques, ChatGPT demonstrates superior accuracy and emerges as a promising tool for Cantonese sentiment analysis. This study also highlights ChatGPT's applicability in real-world scenarios, such as monitoring the quality of text-based counseling services and detecting message-level sentiments in vivo. The insights derived from this study pave the way for further exploration into the capabilities of ChatGPT in the context of underresourced languages and specialized domains like psychotherapy and natural language processing.

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Project description:BackgroundLarge language models (LLMs) are garnering wide interest due to their human-like and contextually relevant responses. However, LLMs' accuracy across specific medical domains has yet been thoroughly evaluated. Myopia is a frequent topic which patients and parents commonly seek information online. Our study evaluated the performance of three LLMs namely ChatGPT-3.5, ChatGPT-4.0, and Google Bard, in delivering accurate responses to common myopia-related queries.MethodsWe curated thirty-one commonly asked myopia care-related questions, which were categorised into six domains-pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis. Each question was posed to the LLMs, and their responses were independently graded by three consultant-level paediatric ophthalmologists on a three-point accuracy scale (poor, borderline, good). A majority consensus approach was used to determine the final rating for each response. 'Good' rated responses were further evaluated for comprehensiveness on a five-point scale. Conversely, 'poor' rated responses were further prompted for self-correction and then re-evaluated for accuracy.FindingsChatGPT-4.0 demonstrated superior accuracy, with 80.6% of responses rated as 'good', compared to 61.3% in ChatGPT-3.5 and 54.8% in Google Bard (Pearson's chi-squared test, all p ≤ 0.009). All three LLM-Chatbots showed high mean comprehensiveness scores (Google Bard: 4.35; ChatGPT-4.0: 4.23; ChatGPT-3.5: 4.11, out of a maximum score of 5). All LLM-Chatbots also demonstrated substantial self-correction capabilities: 66.7% (2 in 3) of ChatGPT-4.0's, 40% (2 in 5) of ChatGPT-3.5's, and 60% (3 in 5) of Google Bard's responses improved after self-correction. The LLM-Chatbots performed consistently across domains, except for 'treatment and prevention'. However, ChatGPT-4.0 still performed superiorly in this domain, receiving 70% 'good' ratings, compared to 40% in ChatGPT-3.5 and 45% in Google Bard (Pearson's chi-squared test, all p ≤ 0.001).InterpretationOur findings underscore the potential of LLMs, particularly ChatGPT-4.0, for delivering accurate and comprehensive responses to myopia-related queries. Continuous strategies and evaluations to improve LLMs' accuracy remain crucial.FundingDr Yih-Chung Tham was supported by the National Medical Research Council of Singapore (NMRC/MOH/HCSAINV21nov-0001).

Dataset Information

What Does ChatGPT Know About Dementia? A Comparative Analysis of Information Quality.

Publications

What Does ChatGPT Know About Dementia? A Comparative Analysis of Information Quality.

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