โก Quick Summary
A recent study analyzed 786,750 posts on X (formerly Twitter) to gauge global sentiment toward health AI at the onset of the ChatGPT era. The findings revealed that 65.26% of the discourse was positive, with significant regional variations in sentiment and concerns, particularly regarding privacy.
๐ Key Details
- ๐ Dataset: 786,750 English-language posts from X (Twitter)
- ๐งฉ Features used: Health and AI-related keywords
- โ๏ธ Technology: LLM-based annotation framework using OpenAI’s GPT-3.5-Turbo and GPT-4
- ๐ Performance: Weighted F1-scores above 0.60 for sentiment classification
๐ Key Takeaways
- ๐ Global sentiment toward health AI is predominantly positive at 65.26%.
- ๐ก Emotional optimism in discussions is high, with 83.62% of posts reflecting positive emotions.
- ๐ Regional differences were notable, with the Eastern Mediterranean and South-East Asia showing higher positive sentiment.
- ๐ Privacy concerns were the most significant, with 33.31% of posts expressing perceived risks.
- ๐ The Americas had a higher focus on algorithms and data governance, at 18.19%.
- ๐ค LLM-powered social listening is a promising method for understanding public sentiment on emerging health technologies.
- ๐ฃ๏ธ The study highlights the importance of public perceptions in the adoption of health AI technologies.
- ๐ Data collected spanned from January 1 to December 5, 2023.
๐ Background
The integration of artificial intelligence (AI) into health care systems is gaining traction, yet public perceptions play a crucial role in determining the acceptance and adoption of these technologies. Social media platforms like X provide a unique opportunity to analyze real-time public discourse, offering insights into how health AI is perceived and discussed.
๐๏ธ Study
This study aimed to explore public sentiment regarding health AI by analyzing a vast dataset of posts from X. By employing large language model (LLM)-based methods, researchers sought to map sentiment, emotional expressions, and confidence signals in discussions about health AI applications. The study represents a pioneering effort in utilizing social media for understanding public attitudes toward health technologies.
๐ Results
The analysis revealed that the overall sentiment regarding health AI was 65.26% positive, with an emotional optimism rate of 83.62%. Notably, the Eastern Mediterranean and South-East Asia regions exhibited significantly higher levels of positive sentiment compared to others. Privacy emerged as a critical concern, with a substantial portion of posts highlighting perceived risks associated with health AI.
๐ Impact and Implications
The findings from this study underscore the importance of understanding public sentiment in the context of health AI. By leveraging LLM-powered social listening, stakeholders can identify dominant narratives and region-specific concerns, which can inform policy development and risk communication strategies. This approach can also be extended to other evolving health technologies, ensuring that public opinions are considered in their implementation.
๐ฎ Conclusion
This study marks a significant step in characterizing online discourse surrounding health AI, revealing both the potential and challenges associated with its adoption. As we move forward, it is essential to address public concerns, particularly regarding privacy, to foster trust and acceptance of AI technologies in health care. Continued research in this area will be vital for responsible governance and effective communication.
๐ฌ Your comments
What are your thoughts on the public sentiment toward health AI? How do you think privacy concerns can be addressed? Let’s engage in a conversation! ๐ฌ Leave your thoughts in the comments below or connect with us on social media:
Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse.
Abstract
BACKGROUND: Artificial intelligence (AI) is increasingly proposed for use in health and health care systems. Beyond technical performance, public perceptions and affective responses influence whether AI technologies are accepted and adopted in real-world contexts. Social media platforms such as X (formerly Twitter) provide large-scale, real-time insight into public discourse surrounding emerging technologies, yet remain underused for examining how health AI is discussed, evaluated, and emotionally framed.
OBJECTIVE: This study aimed to develop and apply large language model (LLM)-based methods for exploratory social listening on health AI. This is the first study to map large-scale sentiment, emotional expressions, and confidence-related signals in online discussions of applications of AI to health.
METHODS: We collected 786,750 English-language posts from X (Twitter) published between January 1 and December 5, 2023, using health- and AI-related keywords. We benchmarked an LLM-based annotation framework by using OpenAI’s GPT-3.5-Turbo and GPT-4, comparing model classifications with trained human researchers. Annotations included overall sentiment and 6 evaluative domains frequently referenced in the literature surrounding attitudes toward health AI-usefulness, safety, privacy, ethics, quality, and trust. After cleaning, GPT-3.5-Turbo used the best-performing prompts to label 388,009 posts. A subset (n=268,347) was further analyzed using Emollama-7b, an open-source model fine-tuned from Meta’s LLaMA2-7B, for emotion detection, and latent Dirichlet allocation for thematic analysis. Comparisons were made across World Health Organization regions.
RESULTS: Compared against human annotations, optimized prompts achieved weighted F1-scores above 0.60 across evaluative domains and sentiment classification. Global discourse about health AI was 65.26% (95% CI 65.11%-65.4%) positive and 83.62% (95% CI 83.48%-83.76%) emotionally optimistic, although substantial regional variation was observed in sentiment (P<.001). The Eastern Mediterranean and South-East Asia regions expressed significantly higher levels of positive sentiment and evaluative agreement in the studied features of health AI, alongside frequent discussion of the tech industry and commercial development. In comparison, the Western Pacific region expressed lower confidence and significantly more mentions of research topics (19.27%, 95% CI 18.5%-20.07%). Privacy was the most prominent global concern, with 33.31% (95% CI 32.98%-33.66%) of privacy-related posts expressing perceived risks. In the Region of the Americas, 18.19% (95% CI 17.92%-18.44%) of posts discussed algorithms and data governance, significantly higher than overall.
CONCLUSIONS: This study offers the first systematic characterization of online health AI discourse at scale, mapping stances toward key features of AI, emotional tone, and discussion topics across regions. LLM-powered social listening is demonstrated as a feasible approach for identifying dominant narratives and regionally distinct concerns, capable of surfacing opinions absent from traditional media. This can extend to studying discourse on other evolving health technologies where public surveying is limited. While methodological refinement and multilingual expansion are needed, this framework can inform timely policy development, risk communication, and responsible health AI governance.
Author: [‘Wass LM’, ‘Wu Z’, ‘Vizoso J’, ‘Wu JT’, ‘Lin L’]
Journal: J Med Internet Res
Citation: Wass LM, et al. Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse. Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse. 2026; 28:e80346. doi: 10.2196/80346