About this Research Topic
The demand for herbal medicine and supplements has increased globally, hence necessitating stringent measures to ascertain their safety and effectiveness. The traditional techniques that have been tried and tested are slow, tedious, moreover prone to errors. This journal’s special edition seeks to unravel how artificial intelligence can modernize the quality assurance systems of natural products by improving accuracy and precision.
Artificial intelligence methods like computer vision coupled with machine learning have introduced novel ways of automating quality evaluations, improving their accuracy. Amazingly, AI could effectively analyze sophisticated data obtained from different sources encompassing chemical, biological as well as imaging data. Consequently, this increases product standardization and safety while reducing operating costs and enhancing scalability worldwide. Moreover, it saves lives because AI-powered quality control can mitigate cases associated with use of unclean or low-quality medicaments made from plants.
Traditional methods of quality control are affected by human bias, slow response times in analysis, and raised costs for the long term. AI can solve these problems by offering stable analysis, which is dependable, and its capability to adjust to new regulatory changes or any modification in the quality of goods. This transformation will increase efficiency and eliminate error tendencies. We want to call for submissions from researchers and practitioners who are working on AI applications in the domain of herbal quality control. The main objective of this Research Topic is to set new standards in the field of quality control of herbal products and foster more research and development in this important sphere.
The Research Topic involves contributions that cover a broad range of topics related to AI and herbal quality assurance. We encourage submissions that address the following themes:
• AI algorithms and models for herbal medicine quality control
• Applications of machine learning in the detection of herbal adulterants and contaminants
• AI-driven techniques for plant identification and authentication
• Innovations in AI-based chromatography and spectroscopy analysis
• Case studies and practical applications of AI in herbal quality control
• Regulatory perspectives and standards in AI-enhanced herbal quality assurance
Note: Please self-assess your MS using the ConPhyMP tool (https://ga-online.org/best-practice/), and follow the standards established in the ConPhyMP statement Front. Pharmacol. 13:953205. All the manuscripts need to fully comply with the Four Pillars of Best Practice in Ethnopharmacology (you can freely download the full version here). Importantly, please ascertain that the ethnopharmacological context is clearly described (pillar 3d) and that the material investigated is characterized in detail (pillars 2 a and b).
If manuscripts submitted the Research Topic to the section Ethnopharmacology of Frontiers in Pharmacology use in silico/artificial intelligence methods, these need to be combined with chemical-analytical approaches in order to assess the quality parameters of a botanical drug and its extracts.
Artificial intelligence methods like computer vision coupled with machine learning have introduced novel ways of automating quality evaluations, improving their accuracy. Amazingly, AI could effectively analyze sophisticated data obtained from different sources encompassing chemical, biological as well as imaging data. Consequently, this increases product standardization and safety while reducing operating costs and enhancing scalability worldwide. Moreover, it saves lives because AI-powered quality control can mitigate cases associated with use of unclean or low-quality medicaments made from plants.
Traditional methods of quality control are affected by human bias, slow response times in analysis, and raised costs for the long term. AI can solve these problems by offering stable analysis, which is dependable, and its capability to adjust to new regulatory changes or any modification in the quality of goods. This transformation will increase efficiency and eliminate error tendencies. We want to call for submissions from researchers and practitioners who are working on AI applications in the domain of herbal quality control. The main objective of this Research Topic is to set new standards in the field of quality control of herbal products and foster more research and development in this important sphere.
The Research Topic involves contributions that cover a broad range of topics related to AI and herbal quality assurance. We encourage submissions that address the following themes:
• AI algorithms and models for herbal medicine quality control
• Applications of machine learning in the detection of herbal adulterants and contaminants
• AI-driven techniques for plant identification and authentication
• Innovations in AI-based chromatography and spectroscopy analysis
• Case studies and practical applications of AI in herbal quality control
• Regulatory perspectives and standards in AI-enhanced herbal quality assurance
Note: Please self-assess your MS using the ConPhyMP tool (https://ga-online.org/best-practice/), and follow the standards established in the ConPhyMP statement Front. Pharmacol. 13:953205. All the manuscripts need to fully comply with the Four Pillars of Best Practice in Ethnopharmacology (you can freely download the full version here). Importantly, please ascertain that the ethnopharmacological context is clearly described (pillar 3d) and that the material investigated is characterized in detail (pillars 2 a and b).
If manuscripts submitted the Research Topic to the section Ethnopharmacology of Frontiers in Pharmacology use in silico/artificial intelligence methods, these need to be combined with chemical-analytical approaches in order to assess the quality parameters of a botanical drug and its extracts.
Keywords: -
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
