ARMR Sciences of New York is trialing a vaccine in the Netherlands to protect against fentanyl-related overdose and death.

The News That Sparked This Post Tempus AI’s acquisition of OneOme’s pharmacogenomics (PGx) assets is making headlines in precision medicine circles. Most observers see this as an oncology play. They’...

If you want evidence-driven insights, humor, and the occasional rant that makes you rethink how we care for people when it matters most, welcome aboard.

OpenAI claims to have figured out what's driving "hallucinations," or AI models' strong tendency to make up answers that are incorrect.

Context: Artificial intelligence (AI), particularly large language models (LLMs), offers the potential to augment clinical decision-making, including in palliative care pharmacy, where personalized treatment and assessments are important. Despite the growing interest in AI, its role in clinical reasoning within specialized fields such as palliative care remains uncertain. Objectives: This study examines the performance of four commercial-grade LLMs on a Script Concordance Test (SCT) designed for pharmacy students in a pain and palliative care elective, comparing AI outputs with human learners’ performance at baseline. Methods: Pharmacy students from 2018 to 2023 completed an SCT consisting of 16 clinical questions. Four LLMs (ChatGPT 3.5, ChatGPT 4.0, Gemini, and Gemini Advanced) were tested using the same SCT, with their responses compared to student performance. Results: The average score for LLMs (0.43) was slightly lower than that of students (0.47), but this difference was not statistically significant (p = 0.55). ChatGPT 4.0 achieved the highest score (0.57). Conclusions: While LLMs show potential for augmenting clinical decision-making, their limitations in patient-centered care highlight the necessity of human oversight and reinforce that they cannot replace human expertise in palliative care. This study was conducted in a controlled research setting, where LLMs were prompted to answer clinical reasoning questions despite default safety restrictions. However, this does not imply that such prompts should be used in practice. Future research should explore alternative methods for assessing AI decision-making without overriding safety mechanisms and focus on refining AI to better align with complex clinical reasoning. In addition, further studies are needed to confirm AI’s comparative effectiveness, given the sample size limitations.

Context: The growing field of palliative care emphasizes the need for high-quality research, yet the contributions of nonphysician palliative care specialists to studying patient outcomes remain under...