Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical
arXiv:2603.18008v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics,
arXiv:2605.08445v1 Announce Type: new Abstract: AI models are increasingly deployed in live clinical environments where they must perform reliably across complex, high-stakes
arXiv:2605.20188v1 Announce Type: cross Abstract: Recommending safe and effective medication combinations from electronic health records (EHRs) is a core clinical AI problem, yet
arXiv:2606.20663v1 Announce Type: new Abstract: Large Language Models have the potential to expand and improve the access to clinical information by enabling new ways of
arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment
arXiv:2603.18459v1 Announce Type: cross Abstract: Medication recommendations aim to generate safe and effective medication sets from health records. However, accurately
arXiv:2604.15456v1 Announce Type: new Abstract: Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and
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