Many efforts have already been made to harness and improve LLMs’ medical knowledge and reasoning capabilities but, to date, the resulting AI is either closed source (e.g. MedPaLM and GPT-4) or limited in scale, at around 13-billion parameters, which restricts their access or ability.
Seeking to improve access and representation, we have developed MEDITRON 7B and 70B, a pair of open-source LLMs with 7 and 70-billion parameters respectively, adapted to the medical domain, and described in their pre-print MEDITRON-70B: Scaling Medical Pretraining for Large Language Models.
Building on the open-access Llama-2 model released by Meta, with continual input from clinicians and biologists, MEDITRON was trained on carefully curated, high-quality medical data sources. This included peer-reviewed medical literature from open-access repositories like PubMed and a unique set of diverse clinical practice guidelines, covering multiple countries, regions, hospitals, and international organizations.
| A Large Language Model for Medical Knowledge