The integration of artificial intelligence into traditional medicine was the focus of a national conference held by the Institute of Teaching and Research in Ayurveda (ITRA) in Jamnagar, Gujarat, on October 16. The event, titled “AyuTech Navonmesh 2026,” formed part of the institute’s sixth Foundation Day programme and brought together experts from Ayurveda, healthcare, academia and technology.
The conference examined how AI and data-driven digital tools might support research, education, medicine development and healthcare delivery in Ayurveda. Discussions also considered the practical and ethical questions involved in applying modern computational technologies to traditional medical systems.
Editorial Insight
Key Highlights
Important points readers should notice.
Issue/Event: National conference on artificial intelligence and Ayurveda under AyuTech Navonmesh 2026.
Location: Institute of Teaching and Research in Ayurveda, Jamnagar, Gujarat.
Authority/Organisation: ITRA and the Ministry of Ayush.
Action Taken: Experts discussed AI applications in research, medicine development, quality assurance, clinical trials, education and digital health.
Impact: The conference encouraged interdisciplinary discussion on digital innovation while highlighting the importance of ethics, data privacy, scientific validation and the integrity of traditional knowledge.
ITRA, an institution under the Ministry of Ayush, organised the conference around the responsible use of artificial intelligence. The programme included discussions on pharmaceutical research, quality assurance, clinical trials, education and the use of digital tools in healthcare. Experts participated in person and through digital platforms.
Research and medicine development among key discussion areas
One focus was the potential use of AI in Ayurvedic medicine research and product development. Data-analysis tools can help researchers organise large datasets, identify patterns and generate questions for further investigation. In pharmaceutical research, computational approaches may also support early-stage screening and the evaluation of research priorities.
Editorial Analysis
Why This Matters
AI could help researchers analyse complex datasets, organise traditional medical information and explore new research questions. These possibilities may support education and research if the systems are developed carefully and evaluated against clear standards. The risks are equally important. Inaccurate outputs, weak datasets, privacy failures or unsupported medical claims could undermine public trust. A digital tool should not be considered reliable merely because it uses AI or draws on a large collection of traditional texts. The conference’s emphasis on responsible use reflects the need to evaluate technology on evidence and outcomes rather than promises. For readers and patients, the practical distinction is that a research discussion or technology demonstration does not establish the clinical effectiveness of a medicine or treatment.
However, AI-generated findings do not independently establish that a medicine is safe or effective. Any proposed application needs appropriate scientific validation, quality controls and relevant testing before it can support clinical or regulatory decisions.
Participants also discussed the possible role of AI in quality assurance and clinical trials. These areas require particular care because the reliability of results depends on the quality of underlying data, research design, reproducibility and transparent reporting. A model trained on incomplete or biased information can produce misleading results even when its output appears convincing.
For Ayurvedic research, an additional challenge is how to represent traditional terminology, formulations, clinical observations and treatment practices in digital systems without losing important context. Researchers need to determine which information can be standardised, how differences in terminology should be handled and how findings can be evaluated against appropriate scientific criteria.
Ayush Grid tools and digital health applications highlighted
Naman Goyal, Officer on Special Duty for Ayush Grid under the Ministry of Ayush, discussed fundamental concepts in artificial intelligence and machine learning, including data collection, processing, model training, testing and prediction.
According to the information shared by the organisers, the presentation also showcased AI-enabled chatbots and digital tools associated with Ayush Grid. Such tools may help users navigate information and digital services, but their usefulness depends on the accuracy of the information provided, the limits of the system and the availability of appropriate human oversight.
In healthcare settings, digital tools should not be treated as substitutes for qualified medical professionals. Systems that provide health-related information need clear boundaries, safeguards for sensitive data and processes for correcting errors. Users should also be able to distinguish general educational information from personalised diagnosis or treatment advice.
WHO experts underline ethical and privacy considerations
Sameer Pujari, associated with digital health and artificial intelligence at the World Health Organization headquarters, addressed AI’s potential role in preserving and organising knowledge. His discussion also covered international collaboration and challenges related to ethics, data privacy and regulation, particularly in traditional medicine.
Dr Geetha Krishnan, who leads work on traditional medicine research, data and innovation at the WHO Global Traditional Medicine Centre, emphasised the importance of understanding Ayurveda’s underlying principles before adopting new technology. In comments to ITRA’s Ayush Next Communication Network, she said AI should complement Ayurveda rather than replace its foundational knowledge and judgement.
Her remarks highlighted an important distinction for the development of digital health systems: using technology to organise or analyse information is not the same as demonstrating that a treatment works. AI can assist research, but clinical claims still require suitable evidence and evaluation.
Data privacy is another consideration. Health information can be sensitive, and systems using patient records or clinical datasets need appropriate consent, access controls, security measures and clear rules governing how information is collected, stored and used.
Wide range of academic and technical expertise
The conference brought together participants from universities, healthcare organisations, research institutions and technology companies. The speakers listed by the organisers included Shubhada Nagarkar of Savitribai Phule Pune University; Dr Rajiv Vasudevan of Apollo Ayurveda Hospitals, Bengaluru; and Dr Sumer Singh of the Indian Institute of Technology Delhi.
Other participants included experts associated with the WHO Global Traditional Medicine Centre, the World Health Organization, IIT Jodhpur, Persistent Systems, C-DAC Pune and Ayur.AI. Their areas of work included digital health, data analytics, healthcare technology, publication ethics, traditional medicine and product development.
The programme also covered responsible AI, scientific evidence, digital knowledge in traditional medicine, innovation and the responsible conduct of clinical trials. Institutional collaborations, memoranda of understanding, expert lectures and recognition programmes were included in the wider Foundation Day celebrations.
The conference provided a platform for discussion and collaboration. It did not, by itself, establish that any particular AI-based Ayurvedic diagnostic system, medicine or treatment had received clinical validation or regulatory approval.
Why independent validation will matter
AI tools used in healthcare can produce outputs that appear precise even when the underlying data or assumptions are flawed. In traditional medicine research, developers must also consider differences in terminology, documentation practices and the interpretation of historical knowledge.
For a proposed AI application to be useful beyond demonstrations, researchers need to establish what task it is designed to perform, which data support it, how its performance is measured and whether results can be reproduced independently. Clinical applications may require further testing to establish safety, effectiveness and suitability for the intended population.
The discussions at ITRA placed these questions alongside the opportunities offered by digital innovation. The next step for individual applications will depend on the evidence produced through research, validation and appropriate oversight.
