Google Research and DeepMind have announced the launch of MedPaLM, an open-sourced large language model aligned to the medical domain.
MedPaLM is meant to generate safe and helpful answers in the medical field. It combines HealthSearchQA, a new free-response dataset of medical questions sought online, with six existing open-question answering datasets covering professional medical exams, research, and consumer queries.
MedPaLM addresses multiple-choice questions and questions posed by medical professionals and non-professionals through the delivery of various datasets. These datasets come from MedQA, MedMCQA, PubMedQA, LiveQA, MedicationQA, and MMLU. A new dataset of curated, frequently searched medical inquiries called HealthSearchQA was also added to improve MultiMedQA.
The HealthsearchQA dataset consists of 3375 frequently asked consumer questions. It was collected by using seed medical diagnoses and their related symptoms.
This model was developed on PaLM, a 540 billion parameter LLM, and its instruction-tuned variation Flan-PaLM to evaluate LLMs using MultiMedQA.
Med-PaLM currently claims to perform particularly well especially compared to Flan-PaLM. It still, however, needs to outperform a human medical expert’s judgment. Up to now, a group of healthcare professionals determined that 92.6 percent of the Med-PaLM responses were on par with clinician-generated answers (92.9 percent).
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