Background: Poisoning remains a notable global health challenge which required rapid and accurate diagnosis to reduce its morbidity and mortality. Conventional toxicological detection methods having limitations by delayed results and restricted sensitivity. Recent digital advancements like artificial intelligence (AI), biosensors, and chromatography–mass spectrometry integration, have transformed poison detection into a rapid, accurate and data-driven process. In Ayurveda, Agad Tantra emphasizes early identification of symptoms of poison and timely intervention. Objective: To analyse digital advancements in poison detection and compare them with principles of Agad Tantra. Methodology: A narrative review was conducted by using contemporary literature on digital toxicology and classical Ayurvedic texts. Comparative analysis was performed to identify parallels between modern technologies and traditional diagnostic approaches such as Prakruti, Satmya, Rutu, Sthana, Vega Bala- Abala, Agni Pariksha and Animal examination. Results: AI-based systems enable predictive modelling and pattern recognition in poisoning cases. Biosensors allow rapid, real-time toxin detection with high sensitivity. Chromatography mass spectrometry ensures accurate qualitative and quantitative analysis, while digital forensic tools enhance medico-legal precision. These advancements parallel Agad Tantra concepts: AI reflects Yukti (logical inference), biosensors align with early detection of Sukshma Visha, and analytical precision corresponds to classification of poisons. Discussion: Digital toxicology and Agad Tantra demonstrate complementary strengths. While digital tools offer accuracy and speed, Ayurveda provides holistic and individualized assessment. Integration may improve early diagnosis and personalized management, though challenges in standardization and validation persist. Conclusion: Digital advancements align closely with Agad Tantra principles. An integrative approach can enhance toxicological diagnostics and improve clinical outcomes.
Rishita Gohel1, Rahul K. Gohil2*, Harsh N. Pandya3, Sushant Sud4 (Wed,) studied this question.