to star items.

Accepted Paper

AI Practice on Tradtional Chinese Medicine  
KaiMan Yu

Send message to Author

Paper short abstract

It investigates how medical Artificial Intelligence presents both an opportunity for communication and transformation, as well as a challenge to establish skills and professional identity for traditional medicine practitioners. And how they strives for modernization' and 'scientization '.

Paper long abstract

Observation, olfaction/listening, inquiry, and palpation constitute the four diagnostic techniques of Traditional Chinese Medicine (TCM). Pulse diagnosis (palpation) is arguably the most mystical and contested. It depends heavily on mentorship and repetitive training, also shapes patients' general imagination and expectations of TCM. Driven by the modernization and scientific validation of TCM, researchers have decoded the 28 clinical pulse types using physical language and developed 'pulse diagnostic devices.' These instruments aim to standardize subjective discrepancies among practitioners, thereby enhancing diagnostic accuracy, consistency, and public trust in TCM.

Clinical practitioners, however, exhibit varied attitudes toward pulse diagnostic devices. The adoption of pulse diagnostic devices in TCM directly implicates practitioners' professional identity and the very survival of it. Given the numerous schools of TCM pulse diagnosis, whether the explanatory models adopted by these devices could reach a consensus remains the core of the debate. Other confounding factors include public expectations of pulse diagnosis and the fact that using these devices is more time-consuming than traditional manual palpation. Through interviews, this study further reveals that physicians at different career stages hold disparate views and varying degrees of reliance on pulse diagnosis. While senior physicians possess extensive experience and do not deem pulse diagnostic devices necessary, younger practitioners—who often experience uncertainty in interpreting tactile signals—find that these devices offer a valuable second reference to support their clinical diagnosis.

Traditional Open Panel P090
Understanding the impact of decision-support AI technologies on medical practice: Learning from empirical studies.
  Session 3