Healthcare artificial intelligence awareness and adoption has increased significantly in the past year, with familiarity rising among hospital executives.

Nine in ten hospitals now have an artificial intelligence strategy in place, and 75 percent of healthcare executives believe AI initiatives are more critical now because of the pandemic, according to a report from Sage Growth Partners.

These results show a significant increase from 2019, when 47 percent had no AI or automation plan in place.

Of the 90 percent of hospitals with an AI strategy, the report showed that 41 percent of organizations are still in the planning stage, while 25 percent are in the early implementation stage.

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Additionally, the report noted that from 2019 to 2020, executive familiarity with automation grew from 50 percent to 66 percent and deployment of automation solutions increased from 23 percent to 34 percent.

Given that most healthcare executives have an AI and automation strategies in place, researchers expect that the use of this technology will grow significantly in the next two years.

“It’s incredibly promising to see the continued and growing adoption of AI within healthcare,” said Sean Lane, CEO of Olive.

“AI solutions are essential pieces of infrastructure at hospitals and health systems, and we have just begun to scratch the surface. There are so many more connections to make using AI– so many more lights to shine on all of the broken healthcare processes that stand between providers and patient care.”

The pandemic has also pushed hospital executives to increasingly rely on AI and automation. Seventy-six percent of respondents said automation has elevated in importance because cutting wasteful spending will help them recuperate and grow faster.

These findings echo those of a November 2020 survey, which showed that 56 percent of healthcare executives said that their response to COVID-19 has caused them to accelerate or expand their AI implementation strategies. The same survey found that 51 percent believe they’ll achieve a return on AI investments faster because of their pandemic response.

In the report from Sage Growth Partners, most respondents who have implemented automation solutions pointed out that the technology is live in only a few areas of their organization.

Among those with existing automation, 59 percent currently use it for five or fewer use cases. Executives also named revenue cycle management, supply chain, and clinical administration as the areas that would benefit most from automation.

Additionally, the executives stated that several barriers still exist to scaling AI and automation. Respondents noted that slow time to implement, lower ROI than expected, and staff constraints are the top challenges to implementation.

Executives also named key criteria for AI and automation success, including selecting the right partner.

“Given that automation is increasingly necessary but difficult to implement and scale, not all organizations will succeed. As several executives noted when interviewed, poorly executed automation can even set the organization back and make driving organizational buy-in for future automation projects more difficult. That makes having the right partner all the more important,” researchers stated.

According to respondents, the top criteria for selecting an automation partner are security and compliance, proven ROI, healthcare expertise, and reliable performance. Healthcare executives also largely prefer a healthcare-focused solution over one spread across multiple industries, with 66 percent of respondents saying they trust a healthcare-focused solution more.

The results demonstrate the industry’s increased focus and adoption of AI, while also highlighting the fact that healthcare still has a ways to go with implementation.

“The pandemic’s impact on financials and staff has heightened the need to invest in efficient and scalable processes, contributing to the burgeoning awareness and usage of AI and automation and moving it from a value-added option to a must-have capability,” the report concluded.

“However, automation has yet to move beyond the pilot stage for most healthcare organizations today, and some are stumbling as they launch projects or expand to other areas. The risk of failure makes selecting the right AI and automation partner all the more critical. Executives increasingly value partners that specialize in healthcare, can achieve ROI, and can serve multiple areas throughout the enterprise as they expand.”

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To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges.

An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania.

Federated learning — an approach first implemented by Google for keyboards’ autocorrect functionality — trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.

A model trained at Penn Medicine, for example, can be distributed to hospitals around the world. Doctors can then train on top of this shared model, by inputting their own patient brain scans. Their new model will then be transferred to a centralized server. The models will eventually be reconciled into a consensus model that has gained knowledge from each of the hospitals, and is therefore clinically useful.

“The more data the computational model sees, the better it learns the problem, and the better it can address the question that it was designed to answer,” Bakas said. “Traditionally, machine learning has used data from a single institution, and then it became apparent that those models do not perform or generalize well on data from other institutions.”

The federated learning model will need to be validated and approved by the U.S. Food and Drug Administration before it can be licensed and commercialized as a clinical tool for physicians. But if and when the model is commercialized, it would help radiologists, radiation oncologists, and neurosurgeons make important decisions about patient care, Bakas said. Nearly 80,000 people will be diagnosed with a brain tumor this year, according to the American Brain Tumor Association.

“Studies have shown that, when it comes to tumor boundaries, not only can different physicians have different opinions, but the same physician assessing the same scan can see different tumor boundary definition on one day of the week versus the next,” he said. “Artificial Intelligence allows a physician to have more precise information about where a tumor ends, which directly affects a patient’s treatment and prognosis.”

To test the effectiveness of federated learning and compare it to other machine learning methods, Bakas collaborated with researchers from the University of Texas MD Anderson Cancer Center, Washington University, and the Hillman Cancer Center at the University of Pittsburgh, while Intel Corporation contributed privacy-protecting software to the project.

The study began with a model that was pre-trained on multi-institutional data from an open-source repository known as the International Brain Tumor Segmentation, or BraTS, challenge. BraTS currently provides a dataset that includes more than 2,600 brain scans captured with magnetic resonance imaging (MRI) from 660 patients. Next, 10 hospitals participated in the study by training AI models with their own patient data. The federated learning technique was then used to aggregate the data and create the consensus model.

The researchers compared federated learning to models trained by single institutions, as well as to other collaborative-learning approaches. The effectiveness of each method was measured by testing them against scans that were annotated manually by neurologists. When compared to a model trained with centralized data that did not protect patient privacy, federated learning was able to perform almost (99 percent) identically. The findings also indicated that increased access to data through data private, multi-institutional collaborations can benefit model performance.

The findings from this study have paved the way for a much larger, ambitious collaboration between Penn Medicine, Intel, and 30 partner institutions, supported by a $1.2 million grant from the National Cancer Institute of the National Institutes of Health that was awarded to Bakas earlier this year. Intel announced in May that Bakas will lead the project, in which the 30 institutions, across nine countries, will use the federated learning approach to train a consensus AI model on brain tumor data. The final goal of the project will be to create an open-source tool for any clinician at any hospital to use. The development of the tool in Penn’s Center for Biomedical Image Computing & Analytics (CBICA) is being led by senior software developer Sarthak Pati, MS.

Study co-author Rivka Colen, MD, an associate professor of Radiology at the University of Pittsburgh School of Medicine, said that this paper and the larger federated learning project open up possibilities for even more uses of Artificial Intelligence in health care.

“I think it’s a huge game changer,” Colen said. “Radiomics is to radiology what genomics was to pathology. AI will revolutionize this field, because, right now, as a radiologist, most of what we do is descriptive. With deep learning, we’re able to extract information that is hidden in this layer of digitized images.”

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