Accelerating Advancements In Healthcare – Asian Scientist Magazine

AsianScientist (Apr. 24, 2024) – Experienced scientists can quickly detect deviations from the norm in experimental cohorts, whether through microscopic images or electrical waves. Similarly, physicians can combine information from various tests to identify signs of disease and provide accurate diagnoses.

Their ability to distinguish between health and illness is due to years of training and practice analyzing biological samples.

Artificial intelligence (AI) models, inspired by the human brain’s learning capabilities, are trained on existing datasets to recognize patterns and apply rules to new samples. This opens up possibilities for healthcare applications such as early disease detection and treatment response predictions.

The training phase is a significant task due to individual variability and symptoms. High-performance computing (HPC) can help overcome this bottleneck by providing substantial computing power for processing large amounts of clinical data quickly.

By combining HPC and AI resources, scientists and physicians aim to understand complex biological phenomena more rapidly and accurately.

&nbsp>Accelerating Drug Development

Modern medicine offers a wide range of drugs for various ailments, but many diseases still lack effective treatments. The drug development process is lengthy, involving identifying targets, testing compounds, and conducting clinical trials.

“Drugs are expensive to develop and can take 10 to 15 years,” said Professor Satoshi Matsuoka, Director of the RIKEN Center for Computational Science. “Automation and shortening development cycles can reduce costs.”

HPC and AI innovations can speed up the drug development pipeline without compromising safety. Molecular dynamics simulations are crucial for understanding how drugs interact with the body. AI algorithms can help search for drug candidates, predict interactions, and optimize structures. Combining HPC accelerates simulations, leading to faster and higher-quality results.

Companies like Taiwan Web Service Corporation (TWSC) are using HPC and deep learning to enhance biomedical workflows. TWSC’s AI supercomputer, Taiwania 2, combined with NVIDIA GPU framework, is revolutionizing bioinformatics and medical imaging.

Through their OneAI platform, TWSC simplifies gene analysis and accelerates drug discovery. The NVIDIA Clara for Drug Discovery deep learning algorithms help predict protein structures and accelerate new drug development.

&nbsp>Generating Drug Candidates

NVIDIA and Mitsui have collaborated on the Tokyo-1 project, using HPC and generative AI (GenAI) models for molecular dynamics. LLMs analyze biological language patterns to associate compounds with specific properties. GenAI designs novel molecular structures for potential therapies, drawing inspiration from viral and tumor cell receptors.

In South Korea, researchers are utilizing HPC to explore new proteins for infectious and neurological diseases, tapping into the creative potential of AI-enabled drug discovery.

Comprising a cluster of V100-GPU cores, their high-performance supercomputing facility expedited the design of a drug to target Interleukin-1 receptor antagonist, which is a key regulator of immunity and inflammation.

Upon testing in the lab using cellular assays, the team found that their designer anti-inflammatory drug showed strikingly better performance than an approved COVID-19 medication, Anakinra. With the first phase of the development process already complete, trials using in vivo pre-clinical models are next on the horizon.

At RIKEN, the AI/HPC pharmaceutical division is also developing a sophisticated platform to span drug discovery until validation, in partnership with several pharmaceutical companies.

“It is not just a single probe or single software,” Matsuoka explained. “The pipeline involves over 50 components, combining software programs, databases and AI algorithms, to generate drug candidates and run simulations to validate the effectiveness of the candidate versus potential dangers.”

&nbsp>A new generation of digital diagnostics

Just as predicting molecular interactions is no easy feat, evaluating a person’s risk for developing disorders is similarly a challenging undertaking.

When assessing heart disease risk, for example, cardiologists must take into account a spectrum of factors, such as age, cholesterol levels and symptoms of chest pain. Co-morbidities like diabetes as well as lifestyle habits like smoking and lack of exercise also contribute to this risk score.

“In general, physicians can roughly predict disease risk, but the accuracy margin is wide,” said Clinical Professor Yeo Khung Keong, CEO and Senior Consultant at the National Heart Centre Singapore, as well as the Academic Chair of the SingHealth Duke-NUS Cardiovascular Sciences Academic Clinical Programme.

At SingHealth, the recently launched AI for the Transformation of Medicine program is poised to bridge this gap, accelerating innovations in smart healthcare through HPC technologies. The Singapore General Hospital campus houses SingHealth’s first-ever supercomputer, CHROMA, which is dedicated to processing vast amounts of clinical data and training Al models for biomedical applications.

Jointly developed with the National Supercomputing Centre Singapore, CHROMA is equipped with 1,024 CPU cores and an NVIDIA DGX 320 GB AI accelerator, and is envisioned to facilitate the development of Al models that can predict disease risk and patient trajectories, as well as support health workers in delivering better care to those most in need.

CHROMA is already making waves in the cardiovascular field, as it is being used to train an Al model that can assess a person’s risk for a serious cardiac event such as a heart attack. The project, dubbed APOLLO, is a collaboration between the National Heart Centre Singapore at SingHealth; the Agency for Science, Technology and Research; Duke-NUS Medical School; National University Hospital; and Tan Tock Seng Hospital.

“[CHROMA and the new innovation center] will catalyze new partnerships between innovators and industry partners, generate new ideas, prototypes and smart technologies for better disease prevention, diagnoses and treatment,” said Professor Ivy Ng, who has since stepped down as Group CEO of SingHealth, in a press release.

Once trained, the AI tool will be able to analyze CT scans of the heart’s vessels to detect narrowing and plaque build-up, which are critical signs that a person may be at risk for developing cardiovascular disease or experiencing a cardiac event in the future. What makes HPC-enabled AI particularly powerful is the possibility to combine different data types, Yeo noted.

For example, the model could learn to take into account other possible biomarkers including the fatty acid composition of the plaques or data from wearables like the heart rate measured by smart watches, especially contextualized to Asian populations. Moreover, CHROMA on its own can shorten the training phase to just one to two months, compared to the half-year it would typically take.

“What AI brings to the table is reducing the variability between assessing risk scores and increasing the speed of getting the reports,” said Yeo. “We want highly reproducible and consistent tools to raise the accuracy of diagnostics.”

These risk assessments can then help guide clinical decision-making, triaging patients with cardiovascular disease and prioritizing those at high risk for serious cardiac events. The APOLLO team envisions that the integration of such technology in the healthcare workflow can lead to better allocation of hospital resources and the timely delivery of potentially life-saving interventions.

Through AI-powered image analytics, digital pathology solutions are also set to revolutionize cancer detection. Typically, tissue samples from patients are laid on microscopy slides for seasoned pathologists to carefully scrutinize under a microscope. However, tiny cancer cells are not easily recognizable, complicating doctors’ efforts to diagnose and assess disease prognosis.

To empower physicians and patients alike, Microsoft and AI company Paige are embarking on a visionary collaboration to develop an image-based GenAI platform that would act as a highly sensitive radar system to spot these malignant cells.

By providing clinical-grade AI and driving the digitalization of modern pathology, the project has the potential to significantly enhance the accuracy and efficiency of clinical oncology work—ultimately enabling precision diagnosis and improving patient outcomes.

&nbsp>Impact and integration

With a vision to build healthier communities, a growing number of countries and institutions are investing in supercomputing resources for biomedical purposes. As HPC-powered healthcare applications pick up speed, it is only a matter of time before these endeavors lead to tangible outcomes for patients.

However, achieving such real-world impact will hinge upon not just technological advancements, but also intentionality and governance over their use. Considering the sensitivity of medical information, Yeo emphasized that regulatory frameworks and practical guidelines must also adapt and evolve alongside these innovations.

“We can aim for integrating AI in healthcare, but because these technologies would affect lives, there must be safeguards and enough evidence that prove their efficacy and safety,” he said.

Accordingly, research teams are incorporating additional security features and privacy-preserving techniques into their workflows, highlighted Matsuoka. Federated learning is one way to keep local databases separate and inaccessible from other users, while still maximizing the efficiency of the model training process on a global server.

Ensuring ethical use and building trust will become important facets to get physicians and patients on board when it comes to integrating novel technologies in the healthcare system.

When anchored on the values of responsible tech, HPC-enabled AI innovations have the power to transform the future of data-driven and needs-based smart medicine—ranging from the lab-centered beginnings of drug discovery, to the ripples of social impact brought about by enhanced diagnostics and clinical care delivery.

“The biggest thing is to integrate AI technologies into our regular workflows so that we hardly even notice it,” said Yeo. “Whether it’s making clinical decisions, monitoring health and alerting patients to warning signs, or predicting outcomes in real-time, supercomputing capabilities would be tremendously important for delivering appropriate medical interventions on an individual level.”

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This article was first published in the print version of Supercomputing Asia, January 2024. Click here to subscribe to Asian Scientist Magazine in print.

Copyright: Asian Scientist Magazine.

Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.

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