• The World Health Organisation (WHO) released a technical brief titled ‘Mapping the Application of Artificial Intelligence in Traditional Medicine’, acknowledging India’s pioneering efforts in integrating AI with traditional medicine systems.

• India has taken steps to incorporate digital technologies into traditional medicine systems such as Ayurveda, Yoga, Naturopathy, Unani, Siddha, Sowa Rigpa and Homeopathy. 

• The government has launched digital platforms and initiatives to provide online consultations, promote digital literacy among practitioners and facilitate the integration of traditional medicine with modern healthcare systems.

• The document showcases a range of AI-driven applications in Ayurveda, Siddha, Unani, Sowa Rigpa, and Homoeopathy, including diagnosis support systems that integrate traditional methods like pulse reading, tongue examination, and Prakriti assessment with machine learning algorithms and deep neural networks. These efforts are enhancing diagnostic accuracy and enabling personalised preventive care.

• One of the standout features in the WHO brief is the mention of Ayurgenomics, a scientific breakthrough that combines genomics with Ayurvedic principles. 

Traditional medicine

• Traditional medicine has a long history. It is the sum total of the knowledge, skill, and practices based on the theories, beliefs, and experiences indigenous to different cultures, whether explicable or not, used in the maintenance of health as well as in the prevention, diagnosis, improvement or treatment of physical and mental illness.

• Its reach encompasses ancient practices such as acupuncture, ayurvedic medicine and herbal mixtures as well as modern medicines.

• The terms “complementary medicine” or “alternative medicine” refer to a broad set of health care practices that are not part of that country’s own tradition or conventional medicine and are not fully integrated into the dominant health care system. They are used interchangeably with traditional medicine in some countries.

• Traditional and complementary medicine (T&CM) is an important and often underestimated health resource with many applications, especially in the prevention and management of lifestyle-related chronic diseases, and in meeting the health needs of ageing populations. 

• Around 80 per cent of the world’s population is estimated to use traditional medicine. As many as 170 of the 194 WHO Member States have reported the use of traditional medicine

• The global popularity of traditional medicine is on the rise, driven by a growing interest in holistic health approaches that emphasize prevention, promotion and rehabilitation.

How AI can be utilised in traditional medicine?

• Cutting-edge computer systems, enhanced with AI and other frontier technologies like machine learning and big data analysis, have the potential to advance personalised medicine by analysing individual health data such as medical records and imaging results and employing predictive analysis, potentially revolutionising holistic health care.

• Machine learning algorithms play a pivotal role in disease diagnosis and prognosis, potentially leading to the application of technology in traditional medicine-based personalised healthcare, which focuses on temperamental analysis or body type assessment. 

• These algorithms can refine decision support systems based on traditional medicine parameters, optimising their performance through the creation of a patient–physician interface leveraging natural language processing (NLP).

• Traditional methods such as pulse diagnosis, tongue examination, urine analysis, speech patterns and touch, as well as energetic assessments in acupuncture and Prakriti (body constitution) assessment in Ayurveda, can also benefit from AI algorithms and deep convolutional neural networks, aiding objective diagnosis and treatment monitoring. 

• Health systems and technical experts in India, for instance, are developing machine learning algorithms to blend the latest knowledge of genomics with Ayurvedic principles with the aim of identifying predictive disease markers that can be used to inform recommendations for personalised preventive approaches. 

• AI can optimise the design of clinical trials for traditional medicine treatments by identifying the most relevant patient groups, appropriate dosages and key outcome measures.

• AI can be utilised for the identification of herbs, mapping of species, standardisation of raw materials and finished formulations, and the analysis of probable herb-herb and herb-drug interactions to guide polypharmacy and integrative health care approaches.

• Large data sets can also be interrogated to assist in the identification of medicinal plants. An AI-driven electronic tongue (e-tongue) can aid plant standardisation by providing objective, reproducible sensory analysis that evaluates taste profiles and quality attributes of herbal and plant-based products.

Traditional Knowledge Digital Library

India was the first country to launch a Traditional Knowledge Digital Library (TKDL) to document and protect Indian traditional medicinal knowledge from its ancient medical practices rooted in Ayurveda, Sidha and Unani. The TKDL is available in several languages and linked to patent search databases. It has extensively digitized text-based formulations of Ayurveda, Unani, Siddha, Sowa Rigpa and practices of Yoga.

Ayurgenomics in India

This project represents an exciting intersection of ayurveda and genomics, aiming to understand the genetic basis of Ayurvedic principles and practices. By integrating genomic data with Ayurvedic principles, the Ayurgenomics project seeks to identify predictive markers for diseases, enabling targeted prevention through personalised health recommendations. Machine learning algorithms are being developed to analyse Ayurvedic constitution types. Additionally, the Ayurgenomics framework is being applied to decipher the genomic and molecular basis of herbal formulations, enabling their repurposing for modern disease conditions.

Challenges in application of AI in traditional medicine

i) Biopiracy threat: Biopiracy refers to the unauthorised appropriation of Indigenous Knowledges, biological resources or traditional cultural expressions, typically for commercial gain. In the context of traditional medicine, this threat arises through the potential exploitation of knowledge and resources without proper acknowledgement, consent or compensation to Indigenous Peoples, the communities and cultures from which the knowledge originates, leading to the loss of cultural autonomy and sovereignty.

ii) Inadequate data infrastructure: AI-enabled health technologies require large volumes of data, either medical or from patients, for training and validation. The absence of robust data and data infrastructure hampers data utilisation. Moreover, the lack of adequate and good-quality data for traditional medicine itself poses a huge risk as model training may lead to inaccurate statements.

Need to develop frameworks

• Despite its rapid growth, the application of AI in traditional medicine remains mostly unexplored. Grey areas exist, requiring further research, evidence, data and debate.

• Understanding AI’s potential, but also its risks and limitations, is crucial to safely approaching the integration of AI into traditional medicine.

• Traditional medicine stands as a crucial pillar in achieving Universal Health Coverage (UHC), given its widespread and sustained utilisation by billions of people worldwide. 

• For many people, traditional medicine is a primary approach, and the first treatment option within reach, in promoting health and well-being for all. 

• AI is already playing a key and rapidly evolving role in health care and is beginning to be used in traditional medicine in innovative and exciting ways. 

• Advancing the use of AI within traditional medicine, and equipping individuals with the knowledge they need to benefit from it, offers an opportunity to tap into the potential of traditional medicine to support the achievement of UHC. 

• However, there is an associated need to develop holistic frameworks tailored to traditional medicine in areas such as regulation, knowledge sharing, capacity building, data governance and the promotion of equity, to ensure the safe, ethical and evidence-based integration of frontier technologies such as AI into the traditional medicine landscape and that the authenticity of the traditional knowledge and the fundamental principles of traditional medicines are not comprised during their translation into the language of AI.

(The author is a trainer for Civil Services aspirants.)