arXiv:2604.01449v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) systems are increasingly integrated into healthcare and pharmacy workflows, supporting tasks such as medication recommendations, dosage determination, and drug interaction detection. While these systems often demonstrate strong performance under standard evaluation metrics, their reliability in real-world decision-making remains insufficiently understood. In high-risk domains such as medication management, even a single incorrect recommendation can result in severe patient harm. This paper examines the relia
The increasing integration of AI into critical domains like healthcare necessitates robust evaluation of its reliability beyond standard metrics, especially as deployment accelerates.
This highlights the growing challenge of ensuring AI safety and trustworthiness in high-stakes environments, directly impacting patient outcomes and regulatory frameworks.
The focus in AI development will shift more intensely towards explainability, robustness, and provable safety measures in addition to performance metrics, particularly in healthcare AI applications.
- · AI safety researchers
- · Healthcare AI ethical review boards
- · AI assurance and auditing firms
- · Providers of interpretable AI solutions
- · Companies deploying unvalidated AI in healthcare
- · Developers focusing solely on performance metrics
- · Patients harmed by unreliable AI systems
Increased scrutiny and regulation of AI systems in healthcare will emerge.
Demand for specialized AI safety and ethics professionals will grow within healthcare and technology sectors.
Public trust in AI will be significantly shaped by the perceived reliability and transparency of AI in critical applications like medicine.
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Read at arXiv cs.LG