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Deploying AI Models at Scale with an Operating System for Smart Hospitals

There is an abundance of market-approved medical AI software that can be used to improve patient care and hospital operations, but we have not yet seen these…

There is an abundance of market-approved medical AI software that can be used to improve patient care and hospital operations, but we have not yet seen these technologies create the large-scale transformation in healthcare that was expected.

Adopting cutting-edge technologies is not a trivial exercise for healthcare institutions. It requires a balance of legal, clinical, and technical risks against the promise of improved patient outcomes and operational efficiency.

Traditionally, the challenges around the adoption of such technologies fell into one of three buckets: people, platforms, and policy. The challenges around a platform for AI adoption are particularly unique given the nature of deep learning technology and the current state of the medical AI ecosystem.

Most deep learning applications have a narrow field of scope. If they stray beyond their domain, they can exhibit unpredictable and unintuitive behavior. This means that to achieve large-scale transformation in medicine, we need thousands of AI applications.

Each of these AI models in production will be communicating information with live clinical systems and making all kinds of inferences that must then be managed. This has the potential of creating an “AI jungle,” with a huge amount of technical debt in an environment where there hasn’t been substantial investment in people to manage such risks.

Another challenge for deploying AI at scale is the lack of interoperability of AI models. Deployment and data integration don’t scale within or across institutions. This lack of interoperability exists within information systems and semantics and between organizations. The result is a high barrier of entry for data scientists and start-ups who don’t have the capacity or domain knowledge to make an impact.

Finally, in recognition of the immaturity of the medical AI economy relative to other subdomains in MedTech, evidence generation must be at the heart of the design of a platform for AI adoption, as many of the AI applications on the market today still require extensive research and analysis of their performance. This is true not only from the perspective of monitoring but also to measure impact on health outcomes.

AIDE: An enterprise approach to AI deployment in healthcare systems

An AI platform that solves these challenges must solve them at the enterprise level to fully capture the benefits of the ‘virtuous cycle of AI’ and to fully mitigate risks around deployment of artificial intelligence.

This ensures the lowered costs of deployment by plugging into a platform that is already integrated to the clinical information systems within healthcare facilities. It also lowers costs around staff and support by creating an opportunity for a single team to service the entire institution, empowered with an enterprise-wide view for managing risks and continuous improvement.

AIDE, developed by the UK Government-funded AI Centre for Value Based Healthcare, is a new operating system for the hospital that allows healthcare providers to deploy AI models safely, effectively, and efficiently. It provides a harmonized hardware and software layer that facilitates the deployment and use of any AI application.

Figure 1. AIDE can receive a live stream of clinical data, allowing clinicians to access near real-time AI analysis within seconds

There are numerous technical risks involved in deploying a large number of models. AIDE mitigates these by providing an administration view that reports every inference of every deployed model as well as performance trend analysis to enable real-time intervention in the case of poor performance.

AIDE also solves the challenge of interoperability by packaging and deploying containerized applications and communicating with the rest of the hospital through standard protocols such as DICOM, HL7, and FHIR.

Clinicians can also review AI inference results with AIDE before they are sent to the patient’s electronic health record (EHR). This clinical review stage can collect useful data around failure instances, which can be fed back to the developer and close the feedback loop.

An open-source standard for healthcare AI with MONAI Deploy

When considering the wide scale adoption of AI, it is important to first consider, as an analogous example, the discovery of X-rays and the subsequent transformation of healthcare through the development of radiology.

After the discovery by Dr Wilhelm Roentgen in 1895 and the famous X-ray of his wife Bertha’s hand, the first uses of X-ray technology were for industrial applications, such as welding inspection, and consumer applications, such as shoe-fitting, rather than medical applications.

Today, most patient experiences involve medical imaging for diagnosis, prognosis, treatment monitoring and more. Rural medical centers can acquire images in the middle of the night and have them reported within an hour by a specialist in another part of the world.

That kind of transformation was only made possible almost 100 years after the invention of the x-ray when the American College of Radiology and National Electrical Manufacturers Association published a standard for the encoding and transfer of medical images, named “Digital Imaging and Communications in Medicine.”

With the birth of the standard in the early 1990s, a transformational journey had begun that would change what would be possible in the art of medicine, leading to advances in oncology, neurology, and many other medical specialties.

Similarly, with deep learning, industrial and consumer applications have raced ahead while medical applications have had limited adoption and even less transformational impact.

That is why the key innovation in AIDE, as an enterprise AI platform, is that it is built on top of the open-source MONAI Deploy architecture.

MONAI Deploy was built to bridge the gap from research innovation to validation and clinical production environments. It gives developers and researchers a default standard, called MONAI Deploy Application Package (MAP), that easily integrates into health IT standards such as DICOM. It also integrates into deployment options across a variety of data center, cloud, and edge environments, making it easy for you to adopt new medical AI applications.

The MONAI Deploy Working Group has defined an open architecture and standard APIs for developing, packaging, testing, deploying, and running medical AI applications in clinical production.

The high-level architecture includes the following components:

  • MONAI Application Package (MAP): Defines how applications can be packaged and distributed.
  • MONAI Informatics Gateway: Communicatesthe clinical information systems and medical devices, such as MRI scanners, over DICOM, FHIR, and HL7 standards.
  • MONAI Workflow Manager: Orchestrates clinical-inspired workflows, composed of AI tasks.

The system has been designed to allow pluggable execution of tasks by different inference engines. The MONAI community is going to keep moving in that direction as well. 

The MONAI Deploy architecture has been co-designed by an international community of hardware, software, academic, and healthcare partners for the mutual aim of standardizing the medical AI lifecycle. This is much in the same way the ACR and NEMA did with medical images three decades ago.

A new era of data-driven medicine

This new layer of informatics, built on top of existing clinical information systems and medical devices, will help usher in the new era of data-driven medicine. For more information about AIDE, see AI Centre for Value Based Healthcare Platforms.

Source:: NVIDIA