For the past few years, we’ve applied core Google AI research and engineering to projects with positive societal impact, including forecasting floods, protecting whales and predicting famine. Artificial intelligence has incredible potential to address big social, humanitarian and environmental problems, but in order to achieve this potential, it needs to be accessible to organizations already making strides in these areas. So, the Google AI Impact Challenge, which kicked off in October 2018, was our open call to organizations around the world to submit their ideas for how they could use AI to help address societal challenges.

“Accelerating social good with artificial intelligence” sheds light on the range of organizations using AI to address big problems. It also identifies several trends around the opportunities and challenges related to using AI for social good. Here are some of the things  we learned—check out the report for more details.

AI is globally relevant 

We received 2,602 applications from six continents and 119 countries, with projects addressing a wide range of issue areas, from education to the environment. Some of the applicants had experience with AI, but 55 percent of not-for-profit organizations and 40 percent of for-profit social enterprises reported no prior experience with AI. 

Similar projects can benefit from shared resources

When we reviewed all the applications, we saw that many people are trying to tackle the same problems and are even using the same approaches to do so. For example, we received more than 30 applications proposing to use AI to identify and manage agricultural pests. The report includes a list of common project submissions, which will hopefully encourage people to collaborate and share resources with others working to solve similar problems.  

You don’t need to be an expert to use AI for social good

AI is becoming more accessible as new machine learning libraries and other open-source tools, such as Tensorflow and ML Kit, reduce the technical expertise required to implement AI. Organizations no longer need someone with a deep background in AI, and they don’t have to start from scratch. More than 70 percent of submissions, across all sectors and organization types, used existing AI frameworks to tackle their proposed challenge. 

Successful projects combine technical ability with sector expertise 

Few organizations had both the social sector and AI technical expertise to successfully design and implement their projects from start to finish. The most comprehensive applications established partnerships between nonprofits with deep sector expertise, and academic institutions or technology companies with technical experience.

ML isn’t the only answer 

Some problems can be addressed by using alternative methods to AI—and result in faster, simpler and cheaper execution. For example, several organizations proposed using machine learning to match underserved populations to legal knowledge and tools. While AI could be helpful, similar results could be achieved through a well-designed website. While we’ve seen the impact AI can have in solving big problems, you shouldn’t rule out more simple approaches as well. 

Global momentum around AI for social good is growing—and many organizations are already using AI to address a wide array of societal challenges. As more social sector organizations recognize AI’s potential, we all have a role to play in supporting their work for a better world. 

Lung cancer is the leading cause of cancer death in the United States and like any cancer, early detection is crucial to survival. Screening at-risk populations is an important part of reducing mortality, and if concerning nodules are found on imaging, further testing may be required. Today, we’ll share how Northwell Health uses Google Cloud products such as Cloud Healthcare APIs and BigQuery to increase caregiver productivity and deliver better care for patients with findings that indicate potential development of lung cancer.

Northwell is New York’s largest healthcare provider

Northwell Health is New York’s largest healthcare provider with 23 hospitals and nearly 800 outpatient facilities. Northwell’s nearly 4,000 doctors care for millions of patients each year, and at this scale, there is an immense amount of healthcare data to manage. To better manage and leverage this data, Northwell Health partnered with Google Cloud starting in 2018.

Enabling caregivers to spend more time with patients

Nic Lorenzen, the lead developer of Northwell Emerging Technology and Innovation team, has a mission to put together data for caregivers in a way that makes sense. It is no secret that inefficient electronic health records systems have a negative impact on a physician’s ability to deliver quality care. Traditional EHRs have information distributed across many tabs, which forces caregivers to spend considerable time at the computer trying to find information. Moreover, speed of care matters. If care is delayed, patients may have to spend more time in the hospital and may suffer worse health outcomes.

To solve this problem, Nic’s team focused on giving caregivers the most relevant pieces of data at the right time by developing an intelligent clinician rounding app. The data needed to derive these insights can depend on the caregiver’s role–a nurse cares about different things than a cardiologist. This system aggregates multiple data sources, and provides patient-specific insights to caregivers.

This system would not have been possible before with traditional EHRs and data warehouses that have proprietary data models and rarely sync data in real time. Now with data easily accessible through Google Cloud’s Healthcare solutions, Nic’s team can deliver the right clinical information to the right people instantly. These days, Nic says, “instead of spending 75% of our time dealing with architecting the underlying platforms, we spend 75% of our time focused on  higher value use cases for clinicians and patients. Google Cloud’s Healthcare solutions have greatly improved our developer productivity and time to value.”

Caregivers have found this new system to be a game changer.Before the implementation of this system, caregivers would spend, on average, seven to nine minutes finding the data needed to make medical decisions for one patient. Now, that aggregated information is delivered to a caregiver’s mobile device in less than a second.

Ensuring patients get the right care with the power of AI

There are a number of reasons why patients might not get the care that they need. For example, patients today can go to multiple hospitals and clinics settings, and coordinating care across multiple facilities is complex. Regional hospitals and clinics have their own siloed view of their data, so pertinent information gathered by one clinic might not be seen by another. These gaps in clinical data lead to gaps in patient care.

When a patient gets radiologic imaging, they may have findings unrelated to the reason they initially got the imaging. For example, a chest CT for a car accident might reveal an incidental lung nodule that could be cancerous. Unfortunately, research shows that a large portion of patients do not get follow up for these incidental findings because it isn’t the primary reason why the patient is seeing a doctor. Moreover, social determinants of health are a factor that affects which patients receive follow-up care. Identifying these patients and providing the necessary follow up care prevents adverse events related to delayed detection of cancer.

With Cloud Healthcare solutions, Northwell built an AI model to identify these patients so that oncologists can appropriately follow up with patients who have findings suspicious for lung cancer. The AI model detects incidental pulmonary nodules in radiology reports so that doctors can then contact the patients that need follow-up care. Nic says his team was able to build this system in a week: “Google Cloud did a lot of heavy-lifting for us and allowed us to get to the AI applications much faster. It allowed us to build a platform that just works.” 

Healthcare systems can now rapidly generate healthcare insights with one end-to-end solution, Google Cloud Healthcare Data Engine. It builds on and extends the core capabilities of the Google Cloud Healthcare API to make healthcare data more immediately useful by enabling an interoperable, longitudinal record of patient data. Northwell Health uses Google Cloud as the core of their platform, enabling their developers to create solutions to the most pressing healthcare problems.

Special thanks to Kalyan Pamarthy, Product Management Lead on Cloud Healthcare and Natural Language APIs for contributing to this blog post.

Gartner® named Google as a Leader in the 2022 Magic Quadrant™ for Cloud AI Developer Services report. This evaluation covered Google’s language, vision and structured data products including AutoML, all of which we deliver through Google Cloud. We believe this recognition is a reflection of the confidence and satisfaction that customers have in our language, vision, and AutoML products for developers. Google remains a Leader for the third year in a row, based upon the completeness of our vision and our ability to execute.

Developers benefit in many ways by using Cloud AI services and solutions. Customers recognize the advantages of Google’s AI and ML services for developers, such as Vertex AI, BigQuery ML, AutoML and AI APIs. In addition, customers benefit from the pace of progress in the field of Responsible AI and actionable ethics processes applied to all customer and partner solutions leveraging Google Cloud technology, as well as our core architecture including the Vertex AI platform, vision, conversational AI, language and structured data, and optimization services and key vertical industry solutions.

We believe that our ‘Leader’ placement validates this vision for AI developer tools. Let’s take a closer look at some of the report findings.

ML tools purpose-built for developers

Google’s machine learning tools have been built by developers, for developers, based on the groundbreaking research generated from Google Research and DeepMind. This developer empathy drives product development, which supports the developer community to achieve deep value from Google’s AI and ML services. An example of this is the unification of all of the tools needed for building, deploying and managing ML models into one ML platform, Vertex AI, resulting in accelerated time to production. They also cite BigQuery ML, AutoML for language, vision video and tabular data) and prebuilt ML APIs (such as speech and translation) as having high utility for developers at all levels of ML expertise to build custom AI and quickly infuse AI into their applications.

Leading organizations like OTOY, Allen Institute for AI and DeepMind (an Alphabet subsidiary) choose Google for ML, and enterprises like Twitter, Wayfair and The Home Depot shared more about their partnership with Google in their recent sessions at Google Next 2021.

Responsible AI principles and practices

Responsible AI is a critical component of successful AI. A 2020 study commissioned by Google Cloud and the Economic Intelligence Unit highlighted that ethical AI does not only prevent organizations from making egregious mistakes, but that the value of responsible AI practices for competitive edge, as well as talent acquisition and retention are notable. At Google, we not only apply our ethics review process to first party platforms and solutions, to ensure that our services design-in responsible AI from the outset, we also consult with customers and partners based on AI principles to deliver accountability and avoid unfair biases. In addition, our best-in-class tools provide developers with the functionality they need to evaluate fairness and biases in datasets and models. Our Explainable AI tools such as model cards provide model transparency in a structured, accessible way, and the What-If Tool is essential for developers and data scientists to evaluate, debug and improve their ML models.

Clear and understandable product architecture

Google Cloud’s investment in our ML product portfolio has led to a comprehensive, integrated and open offering that spans breadth (across vision, conversational AI, language and structured data, and optimization services) and depth (core AI services, with features such as Vertex AI Pipelines and Vertex Explainable AI built on top). Industry-specific solutions tailored by Google for retail, financial services, manufacturing, media and healthcare customers, such as Recommendations AI, Visual Inspection AI, Media Translation, Healthcare Data Engine, add another layer leveraging this foundational platform to help organizations and users adopt machine learning solutions more easily.

At Google Cloud, we refuse to make developers jump through hoops to derive value out of our technology; instead, we bring the value directly to them by ensuring that all of our AI and ML products and solutions work seamlessly together. To download the full report, click here. Get started on Vertex AI and talk with our sales team.

Disclaimer:

Gartner, Magic Quadrant for Cloud AI Developer Services, Van Baker, Arun Batchu, Erick Brethenoux, Svetlana Sicular, Mike Fang, May 23, 2022.

Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Global life science supply chains are lengthy and complex with many moving parts. One small disruption can create serious delays and affect your ability to deliver therapeutics for patients.

Supply chain disruptors

Over the last few years, healthcare organizations have encountered a range of obstacles, from both internal and external factors, that have resulted in supply networks failing to get drugs and medical devices to where they need to be on time. These obstacles include:

• Labor and supply shortages
• Rising material costs
• Raw material constraints
• Geo-political events
• Unpredictable weather

How do you overcome supply chain disruptors that are out of your control?

The intelligent healthcare supply chain

While many organizations have already implemented data-driven supply chains, organizations are still faced with the challenges of static, siloed, and different functional supply chain applications; limited data exchange with key trading partners across upstream and downstream operations; and the inability to effectively leverage relevant external data.

At Google Cloud, we believe the key to meaningful and effective change is a data-driven supply chain that allows you to achieve visibility, flexibility, and innovation.

Our solutions help you prepare for the unpredictable and enhance the value of your data. By unlocking AI-driven insights, you can strengthen distribution networks and optimize your workflows and supply chains to become more reliable, intelligent, and sustainable. Some of the business challenges we address include:

• Predicting demand with Vertex AI Forecast
• Visual inspection for quality and predictive maintenance with pre-built ML models
• Automating and optimizing pickup and delivery operations with Cloud Fleet Routing API
• Real time and holistic inventory visibility with Supply Chain Twin

Make sure you’re prepared for the unpredictable with real-time visibility over your distribution networks. Learn how you can harness the power of AI and analytics and gain actionable insights that enhance your supply chain.

Dr. Elliot Smith cannot be accused of lacking ambition. A high achiever with a Ph.D. in Electrical Engineering and a specialist in magnetic resonance imaging (MRI) systems, Smith aims to deliver top quality healthcare to anyone in the world — regardless of their location or wealth.

Dr. Smith has already made strides on this journey with his Brisbane, Queensland-headquartered business, Maxwell MRI. “I saw there was a big gap in the market around automating the diagnosis of health conditions,” he says. “Existing processes were typically manual and involved a lot of people.”

Artificial intelligence (AI) and machine learning can remove a key obstacle to scaling out medicine and improve the efficiency and accuracy of diagnosing conditions, the healthcare entrepreneur believes.

“Our grand vision is to build an AI doctor that anyone can receive affordable support from and connect to in order to obtain results,” explains Dr. Smith.

Maxwell MRI presently enables clinicians to submit anonymised MRI scans to a machine learning enabled AI platform to help diagnose prostate cancer. The service is sold to clinicians who can then charge a per-session fee to clients. As well as obtaining results for individual cases, the MRI scans and associated information is used to ‘train’ the platform to deliver accurate diagnoses faster and in a more affordable way than existing systems do.

Dr. Smith and his team started by running a number of functions and processes on a single server with graphics processing units (GPUs) and sizable hard disk capacity. However, this infrastructure could not scale to support the planned growth of the business. Each case Maxwell MRI processes involves about 200MB of data in MRI scans alone. Once supplementary data, blood test result, pathology results and genetic information is included, this load can reach more than 1GB of data per patient.

The business aimed to process 150,000 cases by the end of 2018. This required a service that could deliver massive scale in data storage and compute, and could easily be accessed from any location. “We wanted to move from three GPUs to 30 GPUs without having to buy more servers or other associated equipment, so the cloud was the natural next step,” says Dr. Smith.

Maxwell MRI evaluated Google Cloud Platform (GCP) and determined that the managed services component of GCP would remove the burden of infrastructure deployment and administration. In addition, Google Cloud Machine Learning Engine would enable the business to scale to as many GPUs as needed to meet demand.

Maxwell MRI started with some small experiments to determine that GCP met all its requirements and completed its migration to the platform in February 2017. “We really started to scale up the data we had and consequently our computing requirements at that time,” Dr. Smith says.

The Maxwell MRI platform features an upload service that enables clinicians to upload imaging and associated data. This service triggers several different upload pipelines that clean and standardise data. They then write imaging data to Google Cloud Storage, and more structured data to a combination of Google Cloud Datastore and Google Cloud Spanner.

The platform then converts the information into records that can be used to ‘train’ new machine learning configurations or run evaluations through existing machine learning pipelines.

“The tasks we perform including segmenting various anatomical regions for analysis and sending those results back into Google Cloud Storage,” says Dr. Smith. “This then commences that repeated process of running Google Cloud Dataflow pipelines and machine learning algorithms, and presenting those outcomes back to the clinicians.”

Existing Literature Validated

The data processed and analysed to date has, Dr Smith says, enabled Maxwell MRI to help validate existing literature that indicates clinicians lack confidence in existing early-stage testing procedures for prostate cancer. This prompts them to move quickly to the biopsy stage to assure themselves their diagnosis is valid. “New technologies have a lot of potential to rectify this situation and guide treatment to be more accurate, specific and cost-effective,” he says.

Results Delivered in 10-15 Minutes

More specifically, using GCP has enabled Maxwell MRI to guarantee to clinicians that results will be delivered within minutes. “Clinicians are used to getting results back in two days to a week,” says Dr. Smith. “We’re saying that with our platform running on GCP we’ll deliver you results in 10 to 15 minutes, regardless of the number of patients coming in.”

Running on GCP has enabled the business to accelerate its development cycles, test new ideas easily on a subset of data, test in parallel and deliver new services considerably faster than in another environment. In addition, the flexible GCP charging model aligned with the ability to scale compute capabilities quickly and easily has enabled the fledgling business to control its costs.

Google technologies are poised to play an integral role in the business’s future. “With Google available, it doesn’t make sense for us to use our own infrastructure,” Dr. Smith says. “Our expertise in AI, machine learning and clinical engagement complements cloud platform specialties of infrastructure, managed services and ease of use. We see a bright future ahead in helping to transform healthcare globally.”