MedLM: generative AI fine-tuned for the healthcare industry

Improving healthcare and medicine are among the most promising use cases for artificial intelligence, and we’ve made big strides since our initial research into medically-tuned large language models (LLMs) with Med-PaLM in 2022 and 2023. We’ve been testing Med-PaLM 2 with healthcare organizations, and our Google Research team continues to make significant progress, including exploring multimodal capabilities.

Now we’re introducing MedLM — a family of foundation models fine-tuned for healthcare industry use cases. MedLM is now available to Google Cloud customers in the United States through an allowlisted general availability in the Vertex AI platform. MedLM is also currently available in preview in certain other markets worldwide.

Currently, there are two models under MedLM, built on Med-PaLM 2, to offer flexibility to healthcare organizations and their different needs. Healthcare organizations are exploring the use of AI for a range of applications, from basic tasks to complex workflows. Through piloting our tools with different organizations, we’ve learned the most effective model for a given task varies depending on the use case. For example, summarizing conversations might be best handled by one model, and searching through medications might be better handled by another. The first MedLM model is larger, designed for complex tasks. The second is a medium model, able to be fine-tuned and best for scaling across tasks. The development of these models has been informed by specific healthcare and life sciences customer needs, such as answering a healthcare provider’s medical questions and drafting summaries. In the coming months, we’re planning to bring Gemini-based models into the MedLM suite to offer even more capabilities.

Many of the companies we’ve been testing MedLM with are now moving it into production in their solutions, or broadening their testing. Here are some examples.

For the past several months, HCA Healthcare has been piloting a solution to help physicians with their medical notes in four emergency department hospital sites. Physicians use an Augmedix app on a hands-free device to create accurate and timely medical notes from clinician-patient conversations in accordance with the Health Insurance Portability and Accountability Act (HIPAA). Augmedix’s platform uses natural language processing, along with Google Cloud’s MedLM on Vertex AI, to instantly convert data into drafts of medical notes, which physicians then review and finalize before they’re transferred in real time to the hospital’s electronic health record (EHR).

With MedLM, the automated performance of Augmedix’s ambient documentation products will increase, and the quality and summarization will continue to improve over time. These products can save time, reduce burnout, increase clinician efficiency, and improve overall patient care. The addition of MedLM also makes it easier to affordably scale Augmedix’s products across health systems and support an expanding list of medical subspecialties such as primary care, emergency department, oncology, and orthopedics.

Drug research and development is slow, inefficient, and extremely expensive. BenchSci is bringing AI to scientific discovery to help expedite drug development, and it is integrating MedLM into its ASCEND platform to further improve the speed and quality of pre-clinical research and development.

BenchSci’s ASCEND platform is an AI-powered evidence engine that produces a high-fidelity knowledge graph of more than 100 million experiments, decoded from hundreds of different data sources. This allows scientists to understand complex connections in biological research, presenting a thorough grasp of empirically validated and ontologically derived relationships, including biomarkers, detailed biological pathways, and interconnections among diseases. The integration of MedLM works to further enhance the accuracy, precision and reliability, and together with ASCEND’s proprietary technology and data sets, it aims to significantly boost the identification, classification, ranking, and discovery of novel biomarkers — clearing the path to successful scientific discovery.

To spur enterprise adoption and value, we’ve also teamed up with Accenture to help healthcare organizations use generative AI to improve patient access, experience, and outcomes. Accenture brings its deep healthcare industry experience, solutions, and the data and AI skills needed to help healthcare organizations make the most of Google’s technology with human ingenuity.

Aimed at healthcare process improvement, Accenture’s Solutions.AI for Processing for Health interprets structured and unstructured data from multiple sources to automate manual processes that were previously time-consuming and prone to error, like reading clinical documents, enrollment, claims processing, and more. This helps clinicians make faster, more informed decisions and frees up more time and resources for patient care. Using Google Cloud’s Claims Acceleration Suite, MedLM, and Accenture’s Solutions.AI for Processing, new insights can be uncovered — ultimately leading to better patient outcomes.

Healthcare organizations are often bogged down with administrative tasks and processes related to documentation, care navigation, and member engagement. Deloitte and Google Cloud are working together with our mutual customers to explore how generative AI can help improve the member experience and reduce friction in finding care through an interactive chatbot, which helps health plan members better understand the provider options covered by their insurance plans.

Deloitte, Google Cloud, and healthcare leaders are piloting MedLM’s capabilities to make it easier for care teams to discover information from provider directories and benefits documents. These inputs will then help contact center agents better identify best-fit providers for members based on plan, condition, medication and even prior appointment history, getting people faster access to the precise care they need.

As we bring the transformative potential of generative AI to healthcare, we’re focused on enabling professionals with a safe and responsible use of this technology. That’s why we’re working in close collaboration with practitioners, researchers, health and life science organizations, and the individuals at the forefront of healthcare every day. We’re excited by the progress we’ve seen in just one year — from building the first LLM to obtain a passing score (>60%) on U.S. medical-licensing-exam-style questions (published in Nature), to advancing it to obtain an expert level score (86.5%), to applying it in real-world scenarios through a measured approach. As we reflect on 2023 — and close out the year by expanding MedLM access to more healthcare organizations — we’re excited for the progress and potential ahead and our continuing work on pushing forward breakthrough research in health and life sciences.