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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Artificial intelligence and large language models in palliative medicine clinical practice and education
Mark Taubert1, Robyn Hackett2, Simon Tavabie3
1Velindre Cancer Centre, Velindre NHS Trust, Cardiff, UK.
Abstract:
As we approach 2034, we anticipate significant advancements in digital technologies and their impact across various domains, including palliative and end-of-life care and perhaps higher education more generally. Predicting technological breakthroughs, especially in the realm of artificial intelligence (AI), is notoriously difficult. In a sense, you might need an AI to do this effectively. While some digital challenges can surprise us, others prove more elusive than expected. For example, AI's ability to be creative with language and comprehension has been genuinely remarkable and will likely be of interest to those whose 'bread and butter' at work is communication. Similarly, those who teach skills required of clinicians in palliative and end-of-life care, including breaking bad news and nuanced conversations around holistic complexity and treatment preferences are likely to see significant changes and shifts in their practice.
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