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.”

Investing in Artificial Intelligence (AI) can bring a competitive advantage to your organization. If you’re in charge of an AI or Data Science team, you’ll want to measure and maximize the value that you’re providing. Here is some advice from our years of experience in the field. 

A checklist to embark on a project: 

As you embark on projects we’ve found it’s good to have the following areas covered: 

• Have a customer. It’s important to have a customer for your work, and that they  agree with what you’re trying to achieve. Be sure to know what value you’re delivering to them. 
• Have a business case.  This will rely on estimates and assumptions, and may take no more than a few minute’s work.  You should revise this, but always know what justifies your team’s effort, and what you (and your customer) expect to get in return. 
• Know what process you will change or create. You’ll want to put your work in production, so you have to be clear about what business operations are changing or created around your work and who needs to be involved to make it happen
• Have a measurement plan. You’ll want to show that ongoing work is impacting some relevant business indicator. Measure and show incremental value. The goal of these measurements is to establish what has changed because of your project that would otherwise not have changed. Be sure to account for other factors like seasonality or other business changes that may affect your measurements.
• Use all the above to get your organization’s support for your team and your work. 

What measures to use?

As you start the work, what measures and indicators can you use to show that your team’s work is useful for your organization?

How many decisions you make. A major function of ML is to automate and optimize decisions: which product to recommend, which route to follow, etc. Use logs to track how many decisions your systems are making. 

Changes to revenue or costs. Better and quicker decisions often lead to increased revenue or savings. If possible, measure it directly, otherwise estimate it (for example fuel costs saved from less distance traveled, or increased purchases from personalized offers). 

As an example, the Illinois Department of Employment Security is using Contact Center AI to rapidly deploy virtual agents to help more than 1 million citizens file unemployment claims. To measure success the team tracked the two outcomes:  (1) the number of web inquiries and voice calls they were able to handle, and (2) the overall cost of the call center after the implementation. Post implementation, they were able to observe more than 140,000 phone and web inquiries per day and over 40,000 after-hours calls per night. They  also anticipate an estimated annual cost savings of $100M based on an initial analysis of IDES’s virtual agent data (see more in the link to case study).

Implementation costs. The other side of increased revenue or savings, is to put your achievements in the context of how much they cost. Show the technology costs that your team incurs and, ideally, how you can deliver more value, more efficiently. 

How much time was saved.  If the team built a routing system then it saved travel time, if it built an email classifier then it saved reading time, etc. Quantify how many hours were given back to the organization thanks to the efficiency of your system. 

In the medical field, quicker diagnostics matter. Johns Hopkins University’s Brain Injury Outcomes (BIOS) Division has focused on studying brain hemorrhage aiming to improve medical outcomes. The team identified the time to insights as a key metric in measuring business success. They experimented with a range of cloud computing solutions like Dataflow, Cloud Healthcare API, Compute Engine, and AI Platform for distributed training to accelerate iterations. As a result, in their recent work they were able to accelerate insights from scans from approximately 500 patients from 2,500 hours to 90 minutes.

How many applications your team supports. Some of your organization’s operations don’t use ML (say reconciling financial ledgers) but others do. Know how many parts of your organization benefit from the optimization and automation your team builds.

User experience. You may be able to measure your customer’s experience: fewer complaints, better reviews, reduced latency, more interactions, etc. This is valid both for internal and external stakeholders. At Google we measure usage and regularly ask for feedback on any internal system or process.

One of our customers, The City of Memphis, is using VisionAI and ML to tackle a common but very challenging issue: identifying and addressing potholes.  The implementation team identified the percentage increase of potholes identified as one of the key metrics along with accuracy and cost savings. The solution captures video footage from it’s public vehicles and leverages Google Cloud capabilities like Compute Engine, AI Platform, and BigQuery to automate the review of videos.  The project increased  pothole detection by 75% with over 90% accuracy. By measuring and demonstrating these outcomes, the team proved the viability of a cost-effective, cloud-based machine learning model and is looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents. 

Acknowledgements

Filipe and Payam would like to thank our colleague and co-author Mona Mona (AI/ML Customer Engineer, Healthcare and lifesciences) who contributed equally to the writing.