Medicon Medical Sciences (ISSN: 2972-2721)

Review Article

Volume 6 Issue 2


Medical Herbalism Research, Shifting the Paradigm

Alexander Carberry*
University of Birmingham, UK
*Corresponding Author: Alexander Carberry, University of Birmingham, UK.

Published: February 05, 2024

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Abstract  

This paper seeks to establish a paradigm for Medical Herbalism research, harmonious with the manner in which Medical Herbalism is conducted. Researching herbal medicines in controlled experiments with un-standardised extracts to identify causal relationships is inherently difficult. The poly-phytochemical nature and complex interactions of medicinal herbs make definitively identifying active ingredients challenging. The effects and variability of the phytochemical makeup within plant material also complicate controlling for phytochemical consistency and observed effect. Utilising pharmaceutical research methods, where a single chemical can be isolated, standardised and its effect observed, is not possible for the conduct of herbal medicinal research. It requires a significant deviation from the natural variations in plants╩╝ poly- phytochemical profiles occurring within species, which is assumed implicitly in Medical Herbalism.

We propose a paradigm which shifts the observation from the phytochemicals to observable effects, by utilising continuous biomonitoring technology through using Wearable Health Technology. The purpose is to observe quantitative and qualitative patterns of change in actual people engaged in their everyday activities, which is then subjected to Machine Learning analysis of the gathered data to find patterns of physiological change and develop ontological databases of effects. We may overlay the quantitative research with qualitative data to understand the experience of people as a result of the changes. For this we need to explore and develop Medical Herbalism specific approaches.

The necessity of shifting the focus from the poly-phytochemical problem to quantitative changes in the experimental subject opens new horizons. It shifts the focus from causative to statistical correlative methods and provides a platform for more targeted phytochemical profile research, utilising Machine Learning methods such as Semantic Similarity to identify complex effects resulting from variations within the poly- phytochemical profiles of herbs. Such a framework would require ontological databases of effects linked to herbs, providing infrastructure for more targeted phytochemical research.

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