Global English-language-dominated discourse on artificial intelligence in healthcare: a three-year longitudinal analysis of the #AIinHealthcare movement on X

Background: Social media platforms facilitate global discourse on the application of artificial intelligence (AI) in healthcare. Nevertheless, there is a paucity of longitudinal analyses of digitally mediated discussions. Objective: To investigate the evolution of global English-language-dominated discourse on #AIinHealthcare over a three-year period on X (formerly Twitter). Methods: Using Fedica analytics, we analysed 57,880 tweets by 17,991 distinct users across 141 countries from 1 November 2022 to 1 November 2025. This analysis focused on English-language-dominant discourse around #AIinHealthcare (96.9% English), acknowledging hashtag-specific selection bias and linguistic limitations. This study used publicly available anonymised data and followed the ethical guidelines for social media research. Results: The #AIinHealthcare garnered 39.2 million impressions, with significant contributions from high-income countries, notably the United States (40.7%) and Canada (21.0%), as well as India (13.4%; a rapidly expanding economy), collectively accounting for 75.1% of tweets and reflecting hashtag-specific, geographically concentrated engagement. This peaked in mid-2023 and stabilized lower by mid-2025. English was the predominant language of the discourse (96.9%). The community consisted of 74.9% grassroots users with fewer than 1,000 followers, suggesting genuine participation beyond elite influencers. Total engagement reached 72,625 interactions, primarily passive, comprising 68.1% likes, 19.4% retweets, 10.3% replies, and 2.1% quote tweets. Hashtag co-occurrence patterns, supported by qualitative inspection of exemplar tweets, indicated majorly five distinct clusters: foundational technical topics (#GenerativeAI, #ChatGPT, #LLMs) peaked after November 2022; clinical application themes emerged across disease-specific specialties (#Oncology, #Cardiology, #MentalHealth); healthcare implementation themes addressed practical integration (#DigitalHealth, #Telemedicine, #EHR); governance and ethics themes gained prominence (#ResponsibleAI, #AIEthics, #ExplainableAI, #DataPrivacy); and professional integration themes fostered learning communities (#MedTwitter, #MedicalEducation). Sentiment was predominantly neutral (95%), with positive (3%) and negative (2%). Monthly tweets peaked in mid-2023 at 1,600-1,800 before declining to 750-900 per month by June 2025. High-engagement content linked AI to practical applications, governmental initiatives, and clinical breakthroughs. Conclusion: English-language-dominated discourse around #AIinHealthcare reveals hashtag-specific maturation from technical enthusiasm to governance and implementation focus. However, platform access restrictions in countries such as China and Russia may skew geographic representation. Disparities in sustainability discourse remain prevalent.