This paper examines the reliability of AI-assisted medication decision systems, with a focus on how system failures can translate into real-world clinical risks. While AI models often demonstrate strong performance under standard evaluation metrics, such measures may obscure critical failure behaviors in safety-critical domains such as healthcare. Through a structured simulated evaluation of medication scenarios, this work analyses diffrent types of system errors, including false negatives, false positives, and incorrect dosage recommendations. The findings highlight that ceratin errors, particulary undetected harmful interactions, can lead to severe patient outcomes. The study emphasises the importance of moving beyond aggregate performance metrics towards realiability-focused evaluation, considering not only how well AI systems perform, but how they fail and the impact of those failures in real-world settings.
Khalid Adnan Alsayed (Tue,) studied this question.