Healthcare breakthroughs change the world and bring hope to humanity through scientific rigor, human insight, and compassion. We believe AI can contribute to this, with thoughtful collaboration between researchers, healthcare organizations and the broader ecosystem. 

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. 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. 

Med-PaLM 2 harnesses the power of Google’s LLMs, aligned to the medical domain to more accurately and safely answer medical questions. 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%.

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. 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. 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.

Innovating responsibly with AI

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. 

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. In building Med-PaLM 2, we’ve been focused on safety, equity, and evaluations of unfair bias. 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. 

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. 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. 

From AI to generative AI 

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. 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 Cloud is committed to bringing to market products that are informed by our research efforts across Alphabet. 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.

Much more is on the way. In one sense, generative AI is revolutionary. In another, it’s the familiar technology story of more and better computing creating new industries, from desktop publishing to the internet, social networks, mobile apps, and now, generative AI.

Building on AI leadership

Additionally, today we’re announcing a new AI-enabled Claims Acceleration Suite, designed to streamline processes for health insurance prior authorization and claims processing. The Claims Acceleration Suite helps both providers of insurance plans and healthcare to create operational efficiencies and reduce administrative burdens and costs by converting unstructured data into structured data that help experts make faster decisions and improve access to timely patient care. 

On the clinical side, last year we announced Medical Imaging Suite, an AI-assisted diagnosis technology being used by Hologic to improve cervical cancer diagnoses and Hackensack Meridian Health to predict metastasis in patients with prostate cancer. Elsewhere, Mayo Clinic and Google have collaborated on an AI algorithm to improve the care of head and neck cancers, and Google Health recently partnered with iCAD to improve breast cancer screening with AI.

From these examples and more, it’s clear that the healthcare industry has moved from testing AI to deploying it to improve workflows, solve business problems, and speed healing. With this in mind, we expect rapid interest in and uptake of generative AI technologies. Healthcare organizations are eager to learn about generative AI and how they can use it to make a real difference.

Looking ahead

The power of AI has reinforced Google Cloud’s commitment to privacy, security, and transparency. Our platforms are designed to be flexible, including data and model lineage capabilities, integrated security and identity management services, support for third-party models, choice and transparency on models and costs, integrated billing and entitlement support, and support across many languages. 

While we’ll have some innovations like Med-PaLM 2 that are tuned for healthcare, we also have products that are relevant across industries. Last month, we announced several generative AI capabilities coming to Google Cloud, including Generative AI support in Vertex AI and Generative AI App Builder, which are already being tested by a number of customers. Developers and businesses already use Vertex AI to build and deploy machine learning models and AI applications at scale, and we recently added Generative AI support in Vertex AI. This gives customers foundation models they can fine-tune with their own data, and the ability to deploy applications with this powerful new technology. We also launched Generative AI App Builder to help organizations build their own AI-powered chat interfaces and digital assistants in minutes or hours by connecting conversational AI flows with out-of-the-box search experiences and foundation models.

As AI proves its value, it’s likely there will be increased focus on high-quality data collection and curation in healthcare and life sciences. Improving the flow and unification of data across health care systems, referred to as data interoperability, is one of the most important building blocks to leveraging AI, and it helps organizations run more effectively, improve patient care, and helps people live healthier lives. We expect to continue our investments in technology, infrastructure, and data governance.

We’re committed to realizing the potential of this technology in healthcare. By working with a handful of trusted healthcare organizations early on, we’ll learn more about what can be achieved, and how this technology can safely advance. For all of us, the prospects are inspiring, humbling, and exciting. 

If you’re interested in exploring generative AI on Cloud, you can sign-up for our Trusted Tester program or reach out to your Google Cloud sales representative.

AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.

At Cloud Next 2021, we presented how to build easy AI-powered applications with Google Cloud. We introduced a sample application for handling incoming expense reports, analyzing expense receipts with Procurement Document AI, a DocAI solution for automating procurement data capture from forms including invoices, utility statements and more. Then organizing the logic of a report approval process with Workflows, and used Cloud Functions as glue to invoke the workflow, and do analysis of the parsed document.

We also open sourced the code on this Github repository, if you’re interested in learning more about this application.

In the above diagram, there are two user journeys: the employee submitting an expense report where multiple receipts are processed at once, and the manager validating or rejecting the expense report. 

First, the employee goes to the website, powered by Vue.js for the frontend progressive JavaScript framework and Shoelace for the library of web components. The website is hosted via Firebase Hosting. The frontend invokes an HTTP function that triggers the execution of our business workflow, defined using the Workflows YAML syntax. 

Workflows is able to handle long-running operations without any additional code required, in our case we are asynchronously processing a set receipt files. Here, the Document AI connector directly calls the batch processing endpoint for service. This API returns a long-running operation: if you poll the API, the operation state will be “RUNNING” until it has reached a “SUCCEEDED” or “FAILED” state. You would have to wait for its completion. However, Workflows’ connectors handle such long-running operations, without you having to poll the API multiple times till the state changes. Here’s how we call the batch processing operation of the Document AI connector:

  - invoke_document_ai:
    call: googleapis.documentai.v1.projects.locations.processors.batchProcess
    args:
        name: ${"projects/" + project + "/locations/eu/processors/" + processorId}
        location: "eu"
        body:
            inputDocuments:
                gcsPrefix:
                    gcsUriPrefix: ${bucket_input + report_id}
            documentOutputConfig:
                gcsOutputConfig: 
                    gcsUri: ${bucket_output + report_id}
            skipHumanReview: true
    result: document_ai_response

Machine learning uses state of the art Vision and Natural Language Processing models to intelligently extract schematized data from documents with Document AI. As a developer, you don’t have to figure out how to fine tune or reframe the receipt pictures, or how to find the relevant field and information in the receipt. It’s Document AI’s job to help you here: it will return a JSON document whose fields are: line_item, currency, supplier_name, total_amount, etc. Document AI is capable of understanding standardized papers and forms, including invoices, lending documents, pay slips, driver licenses, and more.

A cloud function retrieves all the relevant fields of the receipts, and makes its own tallies, before submitting the expense report for approval to the manager. Another useful feature of Workflows is put to good use: Callbacks, that we introduced last year. In the workflow definition we create a callback endpoint, and the workflow execution will wait for the callback to be called to continue its flow, thanks to those two instructions:

  - create_callback:
    call: events.create_callback_endpoint
    args:
        http_callback_method: "POST"
    result: callback_details
...
- await_callback:
    try:
        call: events.await_callback
        args:
            callback: ${callback_details}
            timeout: 3600
        result: callback_request
    except:
        as: e
        steps:
            - update_status_to_error:
              ...

In this example application, we combined the intelligent capabilities of Document AI to transform complex image documents into usable structured data, with Cloud Functions for data transformation, process triggering, and callback handling logic, and Workflows enabled us to orchestrate the underlying business process and its service call logic.

Going further 

If you’re looking to make sense of your documents, turning dark data into structured information, be sure to check out what Document AI offers. You can also get your hands on a codelab to get started quickly, in which you’ll get a chance at processing handwritten forms. If you want to explore Workflows, quickstarts are available to guide you through your first steps, and likewise, another codelab explores the basics of Workflows. As mentioned earlier, for a concrete example, the source code of our smart expense application is available on Github. Don’t hesitate to reach out to us at @glaforge and @asrivas_dev to discuss smart scalable apps with us.

Retail will forever be an industry that must constantly reinvent itself in response to, and anticipation of, ever-changing consumer demands.

Digital transformation is fueling these changes and we’ve previously spoken about how businesses including Ulta Beauty and Kohl’s are taking advantage of Google Cloud to put data at the center of what they do and deliver the best possible shopping experience and product offerings for their customers.

Leveraging Google Cloud machine learning platform, Sainsbury is able to develop predictive analytics models to spot trends and adjust inventory, providing shoppers with a better experience. 

Sainsbury’s, one of Britain’s best-known supermarkets, is another great example of a business transforming the way it engages with its customers with the cloud.

With over 150 years of service, Sainsbury’s vision is to be the most trusted retailer, where people love to work and shop. It makes customers’ lives easier, by offering great quality and service at fair prices. 

The food industry and the way that customers shop is rapidly changing. From foodie hashtags on Instagram, to the latest cooking fads, customers want to stay connected to the latest trends and Sainsbury’s is empowering them do that.

To help Sainsbury’s achieve this goal, its Commercial and Technology teams, in partnership with Accenture, are building cutting-edge machine learning solutions on Google Cloud Platform (GCP) to provide new insights on what customers want and the trends driving their eating habits.

With the help of Google Cloud Platform, we are generating new insights into how the world eats and lives, to help us stay ahead of market trends and provide an even better shopping experience for our customers.
–Phil Jordan, Group CIO, Sainsbury’s 

Sainsbury’s solution relies on data from multiple structured and unstructured sources. Using Google Cloud’s powerful cloud-based analytics tools to ingest, clean and classify that data, and a custom-built front-end interface for internal users to seamlessly navigate through a variety of filters and categories, Sainsbury’s is able to gain advanced insights in real time.

As a result, Sainsbury’s has been able to develop predictive analytics models to spot trends and adjust inventory, providing shoppers with a better experience. 

Phil Jordan, Group CIO of Sainsbury’s believes this project will have a big impact.

“The grocery market continues to change rapidly. We know our customers want high quality at great value and that finding innovative and distinctive products is increasingly important to them. With the help of Google Cloud Platform, we are generating new insights into how the world eats and lives, to help us stay ahead of market trends and provide an even better shopping experience for our customers.” 

This project is also a great example of the successes Google Cloud customers have when they work with the company’s partners.

“We’re delighted to partner with Google Cloud to help the Sainsbury’s Commercial team apply predictive analytics to the identification of new and emerging trends in grocery,” says Adrian Bertschinger, Managing Director for Retail, Accenture.

“The food sector is experiencing significant, rapid disruption, and this new, cloud-based insights platform will help Sainsbury’s identify trends much earlier and adapt their product assortment in a faster, more informed way—all for the benefit of customers.” 

Whatever the next food or shopping trend may be, Sainsbury’s is looking to the cloud to help them stay a step ahead.