Herbal medicine meets machine learning: a systematic review of AI-powered innovation in chronic inflammation management
Year: 2025
Type: review
Abstract: Chronic inflammatory diseases (CIDs) impose a significant global health burden, affecting over 350 million people and incurring annual healthcare costs exceeding $500 billion. While conventional therapies offer symptomatic relief, they are often limited by adverse effects, high costs, and insufficient efficacy in preventing disease progression. Herbal medicines, rooted in traditional systems, present promising alternatives due to their multi-target anti-inflammatory, antioxidant, and immunomodulatory effects. Bioactive compounds like curcumin, resveratrol, and boswellic acids target critical inflammatory pathways such as NF-κB, MAPK, and JAK-STAT. Despite their potential, challenges like variable bioactive content, poor bioavailability, and limited large-scale clinical trials have hindered widespread adoption. While prior reviews have explored AI in traditional medicine broadly, this review uniquely addresses these limitations by integrating traditional phytotherapy with cutting-edge AI-driven innovations, offering a comprehensive synthesis focused on chronic inflammation management through multi-omics, network pharmacology, and nanotechnology applications that distinguishes it by emphasizing microbiome personalization and regulatory implications. AI optimizes herbal therapies by enhancing bioavailability up to 20-fold through nanoformulations and achieving 95% standardization accuracy for compounds like curcumin and boswellic acids. AI-driven network pharmacology and predictive modeling elucidate complex inflammatory pathways, enabling precision therapeutics with 80–90% predictive accuracy in patient stratification, improving outcomes, and reducing adverse events in conditions such as rheumatoid arthritis and inflammatory bowel disease. By leveraging AI for pharmacovigilance and herb-drug interaction predictions, and employing blockchain-enhanced standardization, this review bridges traditional phytotherapy with modern pharmacology. While advancements are significant, gaps persist in global regulatory frameworks and large-scale, multi-center RCTs. Future research should prioritize AI-integrated RCTs and real-time biomarker monitoring to position herbal therapies as a cornerstone of precision medicine by 2030. Offers a novel integrative framework combining AI-driven multi-omics, network pharmacology, and nanotechnology to optimize herbal therapies for chronic inflammatory diseases, advancing evidence-based phytomedicine. Demonstrates AI’s capacity to enhance bioavailability of phytochemicals like curcumin and boswellic acids by up to 20-fold, achieving 95% standardization accuracy, thus overcoming critical barriers in phytomedicine. Employs AI-driven network pharmacology to map complex inflammatory pathways (e.g., NF-κB, MAPK), enabling 80–90% predictive accuracy in patient stratification for precision therapeutics in rheumatoid arthritis and IBD. Integrates AI-powered pharmacovigilance and blockchain-based standardization, addressing herb-drug interaction risks and regulatory challenges, thereby enhancing the clinical translatability of herbal therapies. Synthesizes robust clinical and preclinical evidence (2010–2025), highlighting herbal therapies’ comparable efficacy to conventional treatments with fewer adverse effects, supported by rigorous RCTs and meta-analyses. Proposes innovative AI-integrated randomized controlled trials (RCTs) with adaptive designs, optimizing trial efficiency by 25% and paving the way for evidence-based phytotherapy in global healthcare systems. Advocates for real-time biomarker monitoring and AI-guided drug repurposing, positioning herbal therapies as a cornerstone of precision medicine by 2030, with significant implications for interdisciplinary research and global health equity.
Source: Discover Applied Sciences
Language: English
Subfield: Complementary and alternative medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4417306592