Medicine is inherently multimodal.

Thrilled to share Med-PaLM M, the first demonstration of a generalist multimodal biomedical AI system with a stellar team @GoogleAI @GoogleDeepMind @GoogleHealth

Paper: https://t.co/ZgEtG0gXEsarxiv.org/pdf/2307.14334…
Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data spanning clinical language, medical imaging, genomics and more performing competently on a diverse array of tasks - all with the same set of model weights.

Pleased to share our latest @GoogleAI @GoogleHealth pre-print on Med-PaLM 2 where we detail our progress towards physician expert-level medical question answering performance!

Link: arxiv.org/pdf/2305.09617… Med-PaLM 2 builds on top of PaLM 2, the latest SOTA LLM from @GoogleAI

We further finetune / align the model to the requirements of the medical domain and introduce ensemble refinement as a simple new prompting strategy to improve the model’s medical reasoning capabilities.

Delighted to share our new @GoogleHealth @GoogleAI @Deepmind paper at the intersection of LLMs + health.

Our LLMs building on Flan-PaLM reach SOTA on multiple medical question answering datasets including 67.6% on MedQA USMLE (+17% over prior work).

arxiv.org/abs/2212.13138 In addition to objective metrics, we pilot a framework for clinician/layperson eval of answers revealing key gaps in Flan-PaLM responses.

To resolve this, we use instruction prompting tuning to further align LLMs to the medical domain and generate safe, helpful answers.