AI in Healthcare

AI IN HEALTHCARE

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THE GLOBAL AI IN HEALTHCARE MARKET WILL EXCEED $64 BILLION BY 2027[1].

Currently, 76% of AI use cases in healthcare are related to diagnosis and treatment systems followed by automated customer service agents and digital assistants[2]. As the world learned during the COVID-19 crisis, medical advancement is dependent on the speed of research. AI solutions helped develop a COVID-19 vaccine in record time. Early on, specialists utilized computer vision solutions to analyze chest x-ray images to diagnose the disease. More recently, natural language processing (NLP) techniques have helped scientists keep track of new advances in COVID-19 treatment.
The White House and a coalition of leading research groups released the COVID-19 Open Research Dataset (CORD-19), which contains over 1,000,000 scholarly articles about COVID-19. A number of NLP models have been used to extract insights from this open dataset on infection and mortality rates in different demographics, symptoms of the disease, identifying suitable drugs for repurposing, and interactions with other diseases.
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Habana offers hardware and software solutions that can increase the pace of research by shortening the time of AI experiment cycles. Habana’s AI solutions reduce R&D costs, letting researchers explore more within their budget.
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Applications of AI in healthcare

Medical image analysis is one of the most popular applications of AI in healthcare.

AI and deep learning algorithms can execute time-sensitive analyses on brain MRIs to detect strokes, chest X-rays to diagnose COVID, histopathology images to detect and monitor cancer tissue, and cardiac ultrasounds. NLP applications in healthcare may be grouped into three broad target segments namely Payer (Health Insurance sector), Provider (hospitals/healthcare delivery), and Pharmaceuticals and Life Sciences (PLS). Broadly, NLP-driven solutions are increasingly used to analyze healthcare text data to reduce human error, driving better outcomes at lower costs.

Challenges

Medical datasets consist of a huge amount of data. There are roughly 1 million annotated open-source chest x-ray images and HIMSS estimates there are approximately 1.2 billion clinical documents produced by the healthcare industry. Training large models[3] on such large datasets takes a significant amount of time, slowing down the pace of research. In healthcare, obtaining faster insights and diagnoses can save lives. Faster training cycles of AI models can reduce costs and improve patient outcomes.
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Why is gaudi a good fit for healthcare Use Cases?

Deep neural network-based medical image analysis or genomics includes a large amount of processing that can be paralleled and thus accelerated. Healthcare use cases benefit specifically from accelerators that can handle data parallelism when the training dataset is huge and model parallelism when the models are large.

Time Cost

The two primary considerations that come into play when employing AI processing are time- and cost-to-train. Habana’s Gaudi Training Processors are expressly designed—in both hardware and software—to deliver high-efficiency cost- and time-to-train, making AI training more accessible to more organizations and for more applications.

First-generation Gaudi delivers up to 40% better price-performance than comparable GPU-based solutions—for both the EC2 DL2 instance and on-premise systems. Training with Gaudi clusters is available both in the cloud with AWS EC2 DL1 instances consisting of 8 Gaudis and on-premises with the Supermicro X12 Gaudi Training Server also consisting of 8 Gaudis. In addition, Gaudi2, which launched in May 2022, offers substantial performance advances that enable significantly faster training of models while preserving cost-efficiency. Gaudi2 systems will be available from Supermicro in 2H 2022 for on-premises implementation.

News and customer testimonials

Leidos built a rich portfolio of healthcare applications for various federal agencies. These include NLP applications for regulatory review, subject matter queries, fraud, waste and abuse detection, and medical literature search and correlation for various government health agencies. They successfully demonstrated 60% cost savings with using DL1 vs. GPU instances training deep learning solution to facilitate medical benefit application processing in Veterans Health agency on DL1 Habana Gaudi instances.

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“Given Leidos and its customers’ need for quick, easy, and cost-effective training for deep learning models, we are excited to have begun this journey with Intel and AWS to use Amazon EC2 DL1 instances based on Habana Gaudi AI processors.”

Chetan Paul
CTO Health and Human Services
Leidos

[1] Global AI in Healthcare Market (2021-2027) by Sections, Diagnosis, End user and Geography. IGR Competitive Analysis, Impact of Covid-19, Ansoff Analysis, available at https://www.researchandmarkets.com/reports/5451294

[2] “IDC’s Worldwide Artificial Intelligence Spending Guide Taxonomy”, IDC, Release V1, 2022

[3] AI in Healthcare: How It’s Changing the Industry”, available at https://www.himss.org/resources/aihealthcare-how-its-changing-industry