AI-Assisted Personalized Herbal Approaches for Maternal Health
Corresponding author: [email protected]
Abstract
Artificial intelligence is transforming healthcare with the promise of precision medicine, predictive analytics and patient-specific treatment strategies. The emergence of evidence-based personalized herbal medicine is a promising approach for the care of mother and baby during pregnancy, with the potential to minimise the side effects associated with conventional methods. This chapter focuses on the use of AI technologies in conjunction with pharmacognosy, phytochemistry and clinical research to develop personalized herbal therapeutic interventions in pregnancy. It discusses the application of machine learning, data-driven decision-making and predictive modelling in assessing the safety, efficacy, teratogenic risk, herb–drug interactions and patient-specific responses of herbal therapies, together with AI-assisted dosage optimization, bioactivity screening of phytochemicals and tailored maternal-health services. Emerging concepts such as digital health monitoring and wearable technologies are also considered, alongside the challenges of data quality, model validation, ethics and regulation that must be addressed to realise safe, personalized herbal medicine for maternal health.
Author Affiliations
- 1 Department of Pharmacology, Divine College of Pharmacy (Savitribai Phule Pune University Affiliated), Nampur Road, Satana, Nashik-423301, Maharashtra, India.
- 2 Department of Pharmaceutical Quality Assurance, Divine College of Pharmacy (Savitribai Phule Pune University Affiliated), Nampur Road, Satana, Nashik-423301, Maharashtra, India.