The article proposes a collaborative approach to build open-source LLMs for healthcare, involving healthcare institutions, academic researchers, clinicians, patients, and technology companies. This consortium could pool resources and expertise to build and fine-tune models that can be transparently evaluated and meet local institutional needs. The article also emphasizes the need for stringent guidelines for data use, measures to prevent data leaks, and strategies to mitigate harm from inappropriate use of LLMs.
Key takeaways:
- Large Language Models (LLMs) like GPT-4 and Google’s Med-PaLM are being integrated into healthcare, with potential applications such as producing clinical notes, assisting with diagnoses, and treatment plans.
- Despite the potential benefits, there are concerns about the control of medicine being ceded to opaque corporate interests, with risks to patient care, privacy, and safety.
- Healthcare institutions, academic researchers, clinicians, patients, and technology companies worldwide are encouraged to collaborate to build open-source LLMs for healthcare, ensuring transparency and inclusivity.
- While LLMs show promise in improving clinical practice and patient experiences, significant challenges remain around deploying them in healthcare settings, including data privacy, model evaluation, and potential biases.