The aim is to outline the research landscape for quality control of herbal medicine using advanced techniques and machine learning.
Conducted a synthesis of existing research
Focused on analytical techniques and machine learning applications
Evaluated current standards in herbal medicine quality control
Identified gaps in current quality assurance practices
Highlighted the potential of data-driven approaches
Suggested future directions for advancing herbal medicine standards
Abstract
This synthesis maps the current research landscape and is a foundational reference for guiding future efforts toward standardized, data-driven, and intelligent quality assurance in HMs.