Philips is looking to leverage large language models (LLMs) to generate clinical notes summarizing calcium scorings, aiming to enhance the accuracy and reliability of automated clinical notes.
Requirements
- Retrieval-Augmented Generation (RAG)
- Knowledge-Augmented Generation (KAG)
Responsibilities
- engaging in a project that aims to leverage large language models (LLMs) to generate clinical notes summarizing calcium scorings.
- collaborate with our clinical specialists and data scientists to develop and evaluate metrics for the automatic generation of these clinical conclusions.
- creating evaluation models, improving prompts, and exploring different LLMs, with the end goal of enhancing the accuracy and reliability of automated clinical notes.
Other
- currently pursuing an undergraduate (BS) degree
- A passion for healthcare and AI, and a strong curiosity about applying technology to solve real-world problems.
- Ability to work independently and manage time effectively in a remote or hybrid setting.
- Enthusiasm for learning new tools and techniques, and a willingness to explore innovative solutions
- US work authorization is a precondition of employment.
- For this position, you must reside in or within commuting distance to Cambridge.
