• Users Online: 271
  • Print this page
  • Email this page

 Table of Contents  
Year : 2023  |  Volume : 7  |  Issue : 5  |  Page : 10-12

Leveraging Artificial Intelligence as a Tool to improve health services and Modernize Ayurveda Treatment – A perspective

Professor and Head Biotechnology (Engineering), Sinhgad College of Engineering affiliated to Savitribai Phule Pune University, Pune, India

Date of Submission12-Apr-2023
Date of Acceptance17-Apr-2023
Date of Web Publication28-Apr-2023

Correspondence Address:
Dr. Kalpana Joshi
Department of Biotechnology (Engineering), Sinhgad College of Engineering, Savitribai Phule Pune University, S. No. 44/1, Off, Sinhgad Rd, Vadgaon Budruk, Pune, Maharashtra 411041
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jras.jras_85_23

Rights and Permissions

How to cite this article:
Joshi K. Leveraging Artificial Intelligence as a Tool to improve health services and Modernize Ayurveda Treatment – A perspective. J Res Ayurvedic Sci 2023;7, Suppl S1:10-2

How to cite this URL:
Joshi K. Leveraging Artificial Intelligence as a Tool to improve health services and Modernize Ayurveda Treatment – A perspective. J Res Ayurvedic Sci [serial online] 2023 [cited 2023 Jun 8];7, Suppl S1:10-2. Available from: http://www.jrasccras.com/text.asp?2023/7/5/10/374512

Artificial Intelligence aids in making robots, Bots, and other machines meant for specific tasks. Through self-learning, machines today can enhance their intelligence to a smarter level. This technology can be harnessed to better the lives of the underprivileged, the marginalized, and the needy. In that programme on Artificial Intelligence, I urged the scientific community to deliberate on how Artificial Intelligence could help us make life easier for our divyang brothers & sisters. Can we make better predictions of natural disasters using Artificial Intelligence? Can we use it to provide assistance to farmers on crop yield? Can Artificial Intelligence be used as a tool to simplify the outreach of health services and modernize medical treatment?

Hon’ble Prime Minister of India Shri Narendra Modi

Mann Ki Baat - 25 Feb, 2018

In the 41st episode of Mann Ki Baat, the Hon’ble Prime Minister raised the question - Can Artificial Intelligence (AI) be used as a tool to simplify the outreach of health services and modernize medical treatment? Ayurveda is a health science, and the question of how AI can be helpful in Ayurveda is relevant. AI is the simulation of the process of human intelligence by computers. It deals with perceiving the environment, solving problems, and acting to achieve a specific goal[1]. John McCarthy is the father of AI, with his extensive experience in computers since he coined the term AI[2].

The deployment of AI in the medical field is growing. A recent review on AI published in Complementary and Alternative Medicine (CAM) analyzed Pubmed, Embase, and Cochrane databases to find studies on AI in CAM. AI was used in diverse fields, such as acupressure treatment, tongue and lip diagnosis, herbal medicines, pulse diagnosis, and TCM syndromes[3]. Another study has reported how thousands of images of Diabetic Retinopathy (DR) were used for training to achieve DR diagnosis with sensitivity and specificity[4]. Furthermore, there has been more and more AI integration happening in the conventional medical field, and so is introduction of AI in complementary and alternative medicine (CAM) is also being attempted[5]

AI has potential to transform ancient science of Ayurveda. Opportunities for interdisciplinary research that joins AI and Ayurveda knowledge base can have breakthrough outcomes in terms of the prediction & prevention of diseases and in delivering personalized therapy. The Trisutra (three fundamental databases) of AI for Ayurveda are human data, Ayurveda therapeutics data, and disease data [Figure 1]. The first dataset on humans includes Dosha Prakriti (Somatic constitution) and its characteristics, Sapta Dhatu (major structural components of the body), 13 types of Agni (digestive/metabolic factors), 13 Srotas (structural or functional body channels), 3 Mala (Body waste products), and Oja (the essence of all seven Dhatu). The second set of Ayurveda therapeutics includes medicinal plants with their Rasa (taste), Guna (property/ quality/ attribute), Virya (Potency), Vipaka (bio-transformed rasa), and procedures like Panchakarma (five internal bio-cleansing therapies) as well as Pathyapathya (compatible diet & regimen). Lastly are the disease symptoms. These three datasets, or Trisutra for AI in Ayurveda, could be brought together to diagnose, predict, and prevent disease through drugs, diet, Dinacharya (daily regimen), and Ritucharya (seasonal regimens) concepts taking care of chronobiology.
Fig. 1: Trisutra for AI in Ayurveda

Click here to view

  Data and AI Top

One of the early examples of a “Decision Support System” in Ayurveda was developed by CDAC Pune, where Ayusoft software was developed for Prakriti diagnosis[6]. Prakriti determines physical and mental characteristics important in deciding a therapeutic regime in Ayurveda. AI can be used to generate intelligent data from Prakriti assessment and fine-tuned for global use. Additionally, Genomic Biomarkers must be added to confirm Prakriti objectively [7],[8]. Several predictions on drugs and diets to bring back health could be based on the AI predictions on Prakriti, Desha (place), and Kala (seasons).

  AI in Phytomedicine- Top

Ayurveda has a rich knowledge base on medicinal plants and their effects on dosha. According to Ayurvedic pharmacology, the drug action is attributed to five principles: Rasa, Guna, Virya, Vipaka, and Prabhava (inconceivable potency). Pharmacologically, the taste is used as a predictor of activity[9]. The Central Council for Research in Ayurvedic Sciences (CCRAS) collected nearly 33,700 references on the 16 selected medicinal plants. The data was displayed as an e-portal entitled “Database on Medicinal Plants” http//:www.nmpb-mpdb.nic.in.

AI models are popularly used for the applications like comparing herbal components, clinical data of patients, and prediction of pathways and prescriptions. Algorithms evaluating herb-induced liver toxicity[10] and ontology-based AI models for side effect prediction[11]are reported. The hierarchical alternative neural network model can capture herbs in a prescription to their efficacy[12]. Deep learning methods have been used to characterize patterns of qi-enriching and blood-enriching herbs to suggest prescriptions by analyzing big-data resources [13]. Such AI insights could be leveraged for R & D in developing new phytomedicines or food products.

  AI in disease data Top

Ayurveda describes a six-stage process of disease manifestation known as “Shatkriyakala.” These six stages can be utilized for diagnosing, preventing, or managing diseases even before their clinical manifestation[14]. Along with genomic and epigenomic markers, screening and assessment by AI to generate proclivity factors will open new ways for very early diagnosis and reversal of diseases like diabetes [Figure 2]. In [Figure 2], each row denotes a barcode generated for one patient with a particular disease. The Yellow color shows Prakriti or normalcy, Red shows disease or Vikriti captured by AI, and Green shows the management in terms of medicine, Panchakarma, or diet prescribed to restore homeostasis.
Figure 2: -AI for pattern recognition for homeostasis

Click here to view

As stated by the Hon’ble Prime Minister, AI can improve health services and modernize treatment in Ayurveda. For this, it is essential to generate robust evidence for applications of AI in Ayurveda therapeutics through the promotion of interdisciplinary research in the field.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Liu, Jiaying & Kong, Xiangjie & Xia, Feng & Bai, Xiaomei & Wang, Lei & Qing, Qing & Lee, Ivan (2018). Artificial Intelligence in the 21st Century. IEEE Access, 2018; 6:34403-21  Back to cited text no. 1
Rajaraman, Vaidy JohnMcCarthy — Father of artificial intelligence. Resonance. 2014;19:198-207.   Back to cited text no. 2
Chu H, Moon S, Park J, Bak S, Ko Y, Youn BY The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Front Pharmacol. 2022;13:826044.   Back to cited text no. 3
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-2410.  Back to cited text no. 4
Khan S. R, Al Rijjal D, Piro A, Wheeler M. B (2021). Integration of AI and Traditional Medicine in Drug Discovery. Drug Discov. Today. 2021; 26 (4):982–992.   Back to cited text no. 5
Rotti H, Raval R, Anchan S, Bellampalli R, Bhale S, Bharadwaj R, et al. Determinants of Prakriti, the Human Constitution Types of Indian Traditional Medicine and its Correlation with Contemporary Science. J Ayurveda Integr Med. 2014;5:167-75.  Back to cited text no. 6
Govindaraj, P, Nizamuddin, S, Sharath, Aet alGenome-wide analysis correlates Ayurveda Prakriti.Sci Rep. 2015; 5:15786.  Back to cited text no. 7
Rotti H, Mallya S, Kabekkodu SP, Chakrabarty S, Hale S et al. DNA Methylation Analysis of Phenotype Specific Stratified Indian Population. Journal of Translational Medicine. 2015; 13:151-162.  Back to cited text no. 8
Kalpana J, Patwardhan B and Hankey A Traditional Phytochemistry: Identification of Drug by Taste. Evid Based Complement Alternat Med. 2007;4(2):145-8.  Back to cited text no. 9
He S, Zhang X, Lu S, Zhu T, Sun G, Sun X A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum Multiflorum Thunb as a Case Study. Biomolecules. 2019; 9 (10): 577.  Back to cited text no. 10
Yao Y, Wang Z, Li L, Lu K, Liu R, Liu Z, Yan J An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example. Comput Math Methods Med. 2019:8617503.   Back to cited text no. 11
Chen L, Liu X, Zhang S, Yi H, Lu Y, Yao P (2021). Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model. BMC. Med. Inform. Decis. Mak. 2021;21 (1): 66.  Back to cited text no. 12
Liu J, Pei M, Zheng C, Li Y, Wang Y, Lu A, et al (2013). A Systems-Pharmacology Analysis of Herbal Medicines Used in Health Improvement Treatment: Predicting Potential New Drugs and Targets. Evid. Based. Complement. Alternat. Med. 2013: 938764.   Back to cited text no. 13
Shirodkar, Jyoti & Sayyad, Mehmood & Nanal, Vilas & Yajnik, Chittaranjan (2014). Anguli Parimana in Ayurveda and its association with adiposity and diabetes. Journal of Ayurveda and integrative medicine. 2014; 5:177-84.  Back to cited text no. 14


  [Figure 1], [Figure 2]


Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  In this article
Data and AI
AI in Phytomedicine-
AI in disease data
Article Figures

 Article Access Statistics
    PDF Downloaded63    
    Comments [Add]    

Recommend this journal