Evaluating the Educational Value of Artificial Intelligence in Pharmacotherapy Education

Final-year medical students frequently make prescribing errors, partly because they have had insufficient training in therapeutic reasoning. Students need to understand the underlying rationale behind a drug choice in order to subsequently apply that knowledge critically and independently. Artificial intelligence (AI) models are becoming increasingly popular among students for therapeutic case solving, but the educational value of these models remains unclear. Therefore, the reasoning process of ChatGPT 3.5 (a general AI model) and EvidenceHunt basic (a specialized medical AI model) was compared with that of a medical specialist/clinical pharmacologist, according to the WHO 6-step. Although both AI models proposed reasonable treatment options, notable differences from those of the medical specialist were observed. The AI models tended to focus primarily on pharmacological recommendations for the main clinical problem, giving less consideration to broader contextual factors, such as the severity of illness. Some inaccuracies and illogical therapeutic alternatives were also identified. While AI models, such as ChatGPT and EvidenceHunt, show potential to improve education in therapeutic reasoning by suggesting plausible treatment options, their reasoning remains limited. They often justify rather than critically evaluate treatment choices, which restricts their depth, especially when it comes to considering alternative therapies and complex patient factors. For AI to be of real educational benefit, students will need to develop reflective skills so that they can critically appraise AI recommendations instead of passively accepting them. Teachers can encourage this by asking students to explain their treatment choices, thereby helping them to strengthen these skills.