International Journal of Homoeopathic Sciences

Machine Learning (ML) technologies are increasingly being integrated into complementary and alternative medicine, offering new possibilities for personalizing homeopathic treatments. This review examines the current applications of ML algorithms in homeopathic practice, focusing on pattern recognition for constitutional type identification and treatment protocol optimization. Through analysis of recent developments in digital health technologies, artificial intelligence applications in complementary medicine, and specific implementations in homeopathic practice, we evaluate the potential benefits and challenges of this technological integration. Current evidence suggests promising applications in patient data analysis and treatment personalization, while highlighting the need for standardized approaches and robust validation studies.