UNDERSTANDING PUBLIC ENGAGEMENT WITH ARTIFICIAL INTELLIGENCE ACROSS HIGH-BURDEN MALIGNANCIES IN THE ASIA-PACIFIC REGION: A SOCIAL LISTENING ANALYSIS
Author(s)
Amanda Woo, PhD1, Keshini Perera, MBA2, Dex Yeoh, BA2, Neeyor Bose, PhD1.
1Ipsos Pte Ltd, Singapore, Singapore, 2Ipsos Sdn Bhd, Kuala Lumpur, Malaysia.
1Ipsos Pte Ltd, Singapore, Singapore, 2Ipsos Sdn Bhd, Kuala Lumpur, Malaysia.
OBJECTIVES: Cancer care is highly complex, relying heavily on sophisticated diagnostics, precision medicine, personalised treatment pathways. The rapid and widespread adoption of artificial intelligence (AI) tools has transformed health-seeking behaviours, making complex medical data easier to understand for laypeople. Consequently, digital community discussions and search patterns have evolved to decode oncology concepts and navigate care decisions. This study characterises public engagement and user intent surrounding AI integration for high-burden cancers, specifically breast cancer, lung cancer, and gastrointestinal cancers, across the Asia-Pacific region.
METHODS: A retrospective social listening analysis was conducted using Ipsos Synthesio’s Discover tool (Beta) to analyse public social conversations between 22 June 2025-22 June 2026 across YouTube, X (Twitter), Instagram, Facebook, Forums and News. A Boolean query strategy involving the intersection of keywords associated oncology and AI was conducted. Public engagement was tracked via the volume of mentions (comments, posts, tweets, etc) and thematically categorised into clusters based on intent. A limitation is that individual user personas (healthcare providers, patients, caregivers) could not be differentiated due to anonymity of public social data.
RESULTS: The search results captured ~316k oncology-related mentions interacting with AI concepts. Breast cancer had the highest volume of mentions (~29.4k), followed by lung cancer (~4.2k) and gastrointestinal cancer (~3k). Semantic topic modelling mapped the AI conversations into three distinct pillars of user intent: early cancer detection and prevention (~17k mentions), clinical decision-making support and precision diagnosis through decoding of clinical data (~13k mentions), and accessibility to personalised treatment (~11k mentions).
CONCLUSIONS: The findings highlight the active engagement of AI-driven health literacy and navigation tools with AI being involved in early detection, prevention of cancer, and personalised treatment for cancer care. This underscores a need for digital health tool developers and healthcare stakeholders to implement guardrails that ensure delivery of medically accurate information and prevent oncology-related misinformation.
METHODS: A retrospective social listening analysis was conducted using Ipsos Synthesio’s Discover tool (Beta) to analyse public social conversations between 22 June 2025-22 June 2026 across YouTube, X (Twitter), Instagram, Facebook, Forums and News. A Boolean query strategy involving the intersection of keywords associated oncology and AI was conducted. Public engagement was tracked via the volume of mentions (comments, posts, tweets, etc) and thematically categorised into clusters based on intent. A limitation is that individual user personas (healthcare providers, patients, caregivers) could not be differentiated due to anonymity of public social data.
RESULTS: The search results captured ~316k oncology-related mentions interacting with AI concepts. Breast cancer had the highest volume of mentions (~29.4k), followed by lung cancer (~4.2k) and gastrointestinal cancer (~3k). Semantic topic modelling mapped the AI conversations into three distinct pillars of user intent: early cancer detection and prevention (~17k mentions), clinical decision-making support and precision diagnosis through decoding of clinical data (~13k mentions), and accessibility to personalised treatment (~11k mentions).
CONCLUSIONS: The findings highlight the active engagement of AI-driven health literacy and navigation tools with AI being involved in early detection, prevention of cancer, and personalised treatment for cancer care. This underscores a need for digital health tool developers and healthcare stakeholders to implement guardrails that ensure delivery of medically accurate information and prevent oncology-related misinformation.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
PCR44
Topic
Health Service Delivery & Process of Care, Patient-Centered Research
Topic Subcategory
Patient Behavior and Incentives, Patient Engagement
Disease
No Additional Disease & Conditions/Specialized Treatment Areas