[29 Sep] AI and Language in Healthcare - HKUArts

The Centre for the Humanities and Medicine would like to cordially invite you to the upcoming session of our Seminar Series, featuring Dr. Max Dunn from The Hong Kong Polytechnic University. Dr. Dunn will be delivering a talk titled "AI and Language in Healthcare: The Role of Doctor Identity and Linguistic Alignment in Perceptions of Doctor Quality”

Event Details

Date: September 29, 2026 (Tuesday)
Time: 16:00 – 17:30 (HKT)
Venue: Room CRT-7.45, 7/F, Run Run Shaw Tower, Centennial Campus, The University of Hong Kong
Registration: https://hkuems1.hku.hk/hkuems/ec_hdetail.aspx?guest=Y&ueid=109658

Please register via the link above or by scanning the QR code on the attached poster to reserve your seat. We look forward to seeing you there!

Abstract

AI will play an increasingly large role in providing healthcare services in the near future, creating a pressing need to examine how people perceive the communicative styles used by AI in healthcare settings compared to humans. This talk covers ongoing research examining perceptions of AI and human doctors in a common medical scenario: giving healthcare advice through an online chat platform. Across five experiments, participants read conversations in which a patient asked either an AI or human doctor for medical advice. The doctor either linguistically aligned with the patient, that being by repeating the medical terms (lexical alignment) or grammatical structures (syntactic alignment) previously used by the patient, or did not align. Linguistic alignment in conversation has been found to increase positive perceptions of interactions and therefore may be a useful tool to increase patient satisfaction in healthcare interactions.

AI doctors but not human ones were penalized when not lexically aligning with the patient, even though these doctors gave the same exact medical advice. However, syntactic alignment and a smaller amount of lexical alignment did not affect perceptions of either doctor. As well, even when the human doctor was described as an inexperienced first year medical student, this doctor was not penalized when not aligning with the patient. Finally, across measures of warmth, competence, trustworthiness, and responsibility, participants generally evaluated AI doctors more negatively than human doctors, with perceptions of AI doctors strongly related to broader attitudes toward robots. Overall, these results suggest that people hold AI to different standards as well as judge AI harsher than humans in medical settings. This talk concludes with a broad discussion on the future of communication with medical chatbots.

Go to [29 Sep] AI and Language in Healthcare [29 Sep] AI and Language in Healthcare 2026-09-28

Go to [29 Sep] AI and Language in Healthcare
[29 Sep] AI and Language in Healthcare

2026-09-28