In 2022, we introduced a deep integration between Google Cloud and Alphabet's AI research organizations, which allows Vertex AI to run DeepMind's groundbreaking protein structure prediction system, AlphaFold. Google Cloud is committed to bringing to market products that are informed by our research efforts across Alphabet. Breakthroughs such as the Transformer have enabled LLMs and other large models to scale to billions of parameters, letting generative AI move beyond the limited pattern-spotting of earlier AIs and into the creation of novel expressions of content, from speech to scientific modeling. Google's deep history in AI informs our work in generative AI technologies, which can find complex relationships in large sets of training data, then generalize from what they learn to create new data. To this end, our AI Principles, established in 2017, form a living constitution that guides our approach to building advanced technologies, conducting research, and drafting our product development policies. What’s more, when Google Cloud brings new AI advances to our products, our commitment is two-fold: to not only deliver transformative capabilities, but also ensure our technologies include proper protections for our organizations, their users, and society. Our limited access for select Google Cloud customers will be an important step in furthering these efforts, bringing in additional expertise across the healthcare and life sciences ecosystem. In building Med-PaLM 2, we’ve been focused on safety, equity, and evaluations of unfair bias. Med-PaLM 2's impressive performance on medical exam-style questions is a promising development, but we need to learn how this can be harnessed to benefit healthcare workers, researchers, administrators, and patients. Since last year, we’ve been researching and evaluating Med-PaLM and Med-PaLM 2, assessing it against multiple criteria - including scientific consensus, medical reasoning, knowledge recall, bias, and likelihood of possible harm - which were evaluated by clinicians and non-clinicians from a range of backgrounds and countries.
They might also explore its utility to help draft short- and long-form responses and summarize documentation and insights from internal data sets and bodies of scientific knowledge. We’re looking forward to working with our customers to understand how Med-PaLM 2 might be used to facilitate rich, informative discussions, answer complex medical questions, and find insights in complicated and unstructured medical texts. Industry-tailored LLMs like Med-PaLM 2 are part of a burgeoning family of generative AI technologies that have the potential to significantly enhance healthcare experiences. As a result, Med-PaLM 2 was the first LLM to perform at an “expert” test-taker level performance on the MedQA dataset of US Medical Licensing Examination (USMLE)-style questions, reaching 85%+ accuracy, and it was the first AI system to reach a passing score on the MedMCQA dataset comprising Indian AIIMS and NEET medical examination questions, scoring 72.3%.
Med-PaLM 2 harnesses the power of Google’s LLMs, aligned to the medical domain to more accurately and safely answer medical questions.
It will be available in coming weeks to a select group of Google Cloud customers for limited testing, to explore use cases and share feedback as we investigate safe, responsible, and meaningful ways to use this technology. Today, we're sharing exciting progress on these initiatives, with the announcement of limited access to Google’s medical large language model, or LLM, called Med-PaLM 2. We believe AI can contribute to this, with thoughtful collaboration between researchers, healthcare organizations and the broader ecosystem. Healthcare breakthroughs change the world and bring hope to humanity through scientific rigor, human insight, and compassion.
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