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Introduction

The traditional Indian system of medicine known as Ayurveda emphasize harmony between the environment, the body, and the mind. For more than 3,000 years, traditional healing practices have been guided by its holistic principles, which are based on the ideas of Tridosha (Vata, Pitta, Kapha), Prakriti (individual constitution), and Swasthya (health)1. Ancient treatises such as the Charaka Samhita, Sushruta Samhita, and Ashtanga Hridaya, as well as countless palm-leaf manuscripts and copper plates, have historically preserved Ayurvedic knowledge, which has been passed down through the generations through oral tradition and carefully scripted manuscripts in Sanskrit2. Ayurveda has long struggled with accessibility, standardization, and integration with traditional healthcare systems, despite its philosophical depth and therapeutic promise3. Its wider application in contemporary science and clinical contexts is limited because a large portion of its wealth of medical knowledge is still restricted to traditional practitioner communities or preserved in archaic texts. However, there are never-before-seen opportunities to close this gap in the digital age. Ayurveda is changing from manuscripts to digital interfaces as a result of the development of telemedicine, artificial intelligence (AI), and information and communication technology (ICT)4. Global access to ancient knowledge has been made possible by the digitization of classical texts, and artificial intelligence (AI) tools are being developed to speed up drug discovery, personalize treatments, and simulate Ayurvedic diagnostics5. Ayurvedic consultation services are being made available to underserved and remote populations at the same time by telehealth platforms6. Ayurvedic education is becoming more accessible thanks to e-learning resources, which give students and practitioners worldwide new opportunities to interact with this age-old science7. Meeting the challenges of the diversified model reaches beyond market concerns. As a context-sensitive, experiential system, Ayurveda relies on the practitioner’s clinical acumen, experience, and rapport with an individual patient8. The attempt to mechanize such an intuitive system raises questions such as the following: 

1. Is it possible for authentic Ayurvedic diagnostic and therapeutic methodologies to be represented through digital frameworks? 
2. Will digital techniques add to or diminish the knowledge-based principle of Ayurveda? 
3. How can ethical bounds be defined when seeking to incorporate ever-advancing health technologies with traditional knowledge systems?

This study aims to examine the journey of Ayurveda’s digital transformation, including the preservation of manuscripts and the development of AI-enabled clinical interfaces. By reviewing current trends, innovations, and challenges in Ayurveda’s digitalization, this paper aims to illustrate how technology affects traditional medicine. The goal is to identify how technology can help preserve knowledge, integrate systems, reach a global audience, and foster innovation while protecting the holistic and cultural ethos of Ayurvedic thought. This inquiry is particularly important today, as global healthcare is increasingly turning into integrative and personalized medicine.

Methods: 

This paper uses a narrative literature review approach. A systematic search was carried out in databases such as PubMed, Scopus, Google Scholar, and the AYUSH Research Portal. The search terms included “Ayurveda digitization,” “Artificial Intelligence in Ayurveda,” “telemedicine and Ayurveda,” “text mining in traditional medicine,” and “ICT in Ayurveda education.” Reviewed sources included peer-reviewed journals, government and institutional reports, and official portals of organizations like CCRAS, CSIR, and the Ministry of AYUSH9. The inclusion criteria focused on works published between 2000 and 2025 that addressed technological integration in Ayurveda. Key themes were organized into three areas: (1) Digitization of classical texts and repositories, (2) AI-assisted diagnosis and drug discovery, and (3) Telehealth and digital education platforms. The search strategy also included literature focusing on data mining applications in traditional medicine, using keywords such as “Ayurveda data mining,” “machine learning in traditional knowledge,” and “text mining Ayurveda.” Peer-reviewed articles from databases like Scopus and Science Direct were prioritized to assess how structured data analysis techniques are being employed in clinical research, textual interpretation, and pharmacovigilance10,11,12,13.

Results:

3.1 Digitization of Classical Ayurvedic Texts-

Major initiatives like the Traditional Knowledge Digital Library (TKDL) have played a pivotal role in cataloging and digitizing Ayurvedic manuscripts. Over 200,000 medicinal formulations have been digitized and made available in multiple languages to prevent biopiracy and support global research. Digital repositories such as the Digital Corpus of Sanskrit (DCS) and the Bhandarkar Oriental Research Institute’s manuscript digitization project have further expanded the availability of Ayurvedic texts14. Text mining technologies, which use Natural Language Processing (NLP), are extracting data from Sanskrit texts for use in pharmacoinformatics and disease ontology mapping. 

3.2 Artificial Intelligence in Ayurvedic Diagnosis and Treatment-  

AI-based tools are emerging as significant contributors in Ayurvedic diagnostics. Machine learning algorithms are being used to create predictive models for Prakriti analysis, dosha imbalances, and herbal drug interactions. The digitization of Nadi Pariksha (pulse diagnosis) and Jihva Pariksha (tongue diagnosis) allows for greater standardization and reproducibility. AI platforms are also aiding drug discovery through phytochemical analysis and predicting the synergy of polyherbal formulations, which cuts down the time and cost needed in experimental pharmacology. 

3.3 Telehealth and e-Learning in Ayurveda-  

The rise of telemedicine platforms like eSanjeevani and mobile consultation apps has provided wider access to Ayurvedic healthcare, especially in rural areas. These systems include patient intake forms based on dosha assessment, offer lifestyle advice, and allow for digital follow-ups. At the same time, e-learning tools have been adopted in Ayurvedic institutions. Platforms like CCRAS e-Learning, Rashtriya Ayurveda Vidyapeeth (RAV) online programs, and various MOOCs offer video lectures, case-based simulations, and different exam preparation tools and methods to students worldwide. 

3.4 Data Mining and Knowledge Extraction from Ayurvedic Sources-

The digitization of Ayurvedic manuscripts and clinical data has led to the growing use of data mining techniques to derive actionable patterns from unstructured information. Using Natural Language Processing (NLP) and machine learning, researchers have started extracting knowledge from Sanskrit texts to identify disease-treatment patterns, drug synergy, and clinical correlations. For instance, Sankar et al. (2021) demonstrated how frequent pattern mining was used to evaluate Tridosha-based symptom clusters from historical case records, aiding predictive modeling of diseases. Furthermore, association rule mining has been applied to herbal formulations to detect commonly used drug pairs and polyherbal relationships. Recent research also integrates data mining in real-world Ayurvedic hospital datasets to evaluate treatment effectiveness, adverse event patterns, and demographic trends (Rajeev et al., 2020). This research complements AI initiatives and strengthen evidence-based Ayurveda.

Discussion

The digitalization of Ayurveda represents a significant change from sacred manuscripts to modern technologies. It creates opportunities to expand age old knowledge, promote global acceptance, and update clinical practices while maintaining the key principles of personalized and preventive healthcare. AI particularly allows for in-depth data analysis for diagnosis, customization, and research on herbal formulations.  However, several challenges persist: 

• Epistemological mismatch: Ayurveda’s qualitative and experiential frameworks often conflict with the quantitative logic of AI systems. 
• Data ethics: Privacy, consent, and the cultural sensitivity of traditional knowledge must be treated with sensitivity. 
• Standardization difficulties: Variability in classical interpretations and complex language present obstacles in digitization and algorithm development. 

Addressing these challenges needs a collaborative approach that includes traditional Vaidyas, AI engineers, linguists, legal scholars, and global health policymakers. The future of Ayurveda in the digital age looks promising. Potential developments include: 

• AI-integrated diagnostic devices that monitor variations in doshas continuously. 
• Global clinical databases of Ayurvedic case studies for research, training, and algorithm development. 
• Virtual reality (VR) and augmented reality (AR) tools for immersive Ayurvedic education and practitioner training. 
• Integration of Ayurveda with modern electronic medical records (EMRs) and global health informatics systems. 
• Blockchain-based systems for protecting traditional knowledge, ensuring secure sharing of Ayurvedic intellectual property. 

If implemented thoughtfully and ethically, these innovations can upgrade Ayurveda to a leading role in integrative, patient-focused, and sustainable global healthcare. Data mining offers a transitional framework between traditional Ayurvedic texts and modern AI applications. It enables for the systematic extraction of implicit relationships—such as, Dosha-Prakriti imbalances and associated herbal responses—from thousands of years of textual and clinical data. However, challenges persist due to:

Lack of annotated corpora in Sanskrit.

Inconsistent documentation in historical and contemporary clinical records.

Cultural subtleties that need expert interpretation during rule extraction. A hybrid approach that pairs machine intelligence with human expertise, particularly from expert Vaidyas, is crucial to ensure the accuracy, context, and ethical use of the mined data.

Conclusion

Ayurveda’s digital evolution, from ancient palm-leaf manuscripts to algorithmic interfaces, is not just a technological change but a fundamental shift in perspective. AI, telehealth, and digital repositories provide unique opportunities for standardization, global sharing, and scientific investigation. Data mining acts as a bridge between Ayurveda’s qualitative principles and quantitative digital tools.

It allows for in-depth exploration of classical texts and modern datasets, fostering clinical innovation and predictive modeling in integrative healthcare. At the same time, it is crucial to preserve the intuitive, experiential, and ethical foundations of Ayurveda. A balanced approach rooted in tradition and driven by innovation is essential for ensuring Ayurveda thrives in the digital age.