EVALUATING PERCEPTIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE ACROSS THE ASIA-PACIFIC REGION: A SOCIAL LISTENING AND REAL-WORLD EVIDENCE STUDY
Author(s)
Neeyor Bose, PhD1, Keshini Perera, MBA2, Dex Yeoh, BA2, Amanda Woo, PhD1.
1Ipsos Pte Ltd, Singapore, Singapore, 2Ipsos Sdn Bhd, Kuala Lumpur, Malaysia.
1Ipsos Pte Ltd, Singapore, Singapore, 2Ipsos Sdn Bhd, Kuala Lumpur, Malaysia.
OBJECTIVES: Both consumers and healthcare professionals (HCPs) are increasingly looking to artificial intelligence (AI) to supplement healthcare delivery. Particularly in parts of the Asia-Pacific (APAC) region, traditional patient-provider dynamics can limit shared decision-making or patient inquiry regarding test results and care options. This study aimed to evaluate public and professional attitudes toward using AI in healthcare across seven APAC markets: Australia, Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam.
METHODS: A social listening approach using Ipsos Synthesio’s Discover tool (Beta) extracted insights from public social data across YouTube, X (Twitter), Instagram, Facebook, healthcare forums, and news media sites. Publicly available digital posts from the seven countries dated June 22, 2025, to June 22, 2026, were extracted and analyzed thematically. The search scope was restricted to four key therapeutic areas: oncology, type-2 diabetes, dermatology, and mental health conditions.
RESULTS: The search yielded ~810k healthcare AI-related content - including but not limited to posts, threads, mentions etc. Volume of posts distribution by condition was dominated by oncology (39%), followed by mental health (19%), diabetes (5%), and dermatology excluding cosmetics (1.3%). Oncology discourse was primarily HCP-driven, whereas mental health and diabetes discussions were predominantly patient-led. Three primary positive themes emerged: faster, accurate diagnosis (58%); improved healthcare access (36%); and early detection/preventive care (19%). Conversely, key negative themes highlighted lack of trust in diagnostic accuracy (9%), data protection risks (8%), and ethical implementation concerns (5%).
CONCLUSIONS: Substantial optimism exists surrounding health-related AI utility in the APAC region. Consumers and HCPs anticipate net-positive outcomes of AI use in healthcare provided robust guardrails and regulations are put in place to govern diagnostic accuracy, data privacy, and ethical frameworks. Implications of these findings include the potential for AI to bridge healthcare access gaps, empower patient autonomy, and improve clinical diagnostic throughput.
METHODS: A social listening approach using Ipsos Synthesio’s Discover tool (Beta) extracted insights from public social data across YouTube, X (Twitter), Instagram, Facebook, healthcare forums, and news media sites. Publicly available digital posts from the seven countries dated June 22, 2025, to June 22, 2026, were extracted and analyzed thematically. The search scope was restricted to four key therapeutic areas: oncology, type-2 diabetes, dermatology, and mental health conditions.
RESULTS: The search yielded ~810k healthcare AI-related content - including but not limited to posts, threads, mentions etc. Volume of posts distribution by condition was dominated by oncology (39%), followed by mental health (19%), diabetes (5%), and dermatology excluding cosmetics (1.3%). Oncology discourse was primarily HCP-driven, whereas mental health and diabetes discussions were predominantly patient-led. Three primary positive themes emerged: faster, accurate diagnosis (58%); improved healthcare access (36%); and early detection/preventive care (19%). Conversely, key negative themes highlighted lack of trust in diagnostic accuracy (9%), data protection risks (8%), and ethical implementation concerns (5%).
CONCLUSIONS: Substantial optimism exists surrounding health-related AI utility in the APAC region. Consumers and HCPs anticipate net-positive outcomes of AI use in healthcare provided robust guardrails and regulations are put in place to govern diagnostic accuracy, data privacy, and ethical frameworks. Implications of these findings include the potential for AI to bridge healthcare access gaps, empower patient autonomy, and improve clinical diagnostic throughput.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
PCR216
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