ACCURACY AND TEACHABILITY OF ARTIFICIAL INTELLIGENCE CHATBOTS IN SOLVING PHARMACEUTICAL CALCULATIONS: A DESCRIPTIVE STUDY - KUMAR, K. G. SATHEESH; BHARATHI, UPPU (International Journal of Current Pharmaceutical Research, 2026)
Objectives: This study evaluated the accuracy and teachability of widely available AI chatbots in performing pharmaceutical calculations, including adult and paediatric dosing, IV infusion rates, and dilution/concentration problems. Methods: A descriptive study was conducted in the Department of Pharmacology, Sri Venkateswara Medical College, Tirupati. Five free-access AI chatbots (A–E) were given 35 questions each. Responses were compared with gold standard answers. Incorrect answers were corrected through feedback, and performance was reassessed. Data were analysed descriptively using Microsoft Excel. Results: Accuracy varied across chatbots, with Chatbot A performing best (89%) and Chatbot D close behind (86%). All chatbots performed well in simpler tasks such as percentage and ratio calculations (86%), whereas paediatric dosing, IV infusion, and dilution/concentration problems were more error-prone. Following feedback, teachability was high: Chatbots A, B, and D corrected all errors (100%), while C and E improved to 91% and 82%, respectively. Conclusion: AI chatbots show potential as educational and clinical support tools for pharmaceutical calculations. They handle simpler tasks reliably and can improve performance after feedback. Nevertheless, supervision remains crucial for complex calculations to ensure patient safety. When integrated carefully, AI chatbots can complement traditional learning and clinical practice.