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The social value of high-performance computing (HPC) is becoming increasingly obvious. It offers the computing power needed to help humanity solve some of its greatest challenges. Using HPC in healthcare is relatively new: traditionally, it has been dominated by physicists, chemists, and astrophysicists. Applying this technology to healthcare, however, benefits not just individuals but society as a whole. Artificial intelligence (AI) also has an important part to play in the public health domain, the administration of healthcare, as well as the clinical setting, particularly when it comes to the automation of diagnosis processes. Combined with HPC, these technologies will transform the future of healthcare.
HPC is a perfect fit for healthcare because of the large data sets generated by public health systems, much of which is in the form of images (from scans, for example). These data sets constitute a vast body of knowledge which has been largely unexplored until now. Most of their value comes from interpretations by experts, which has been done manually and is a hugely limiting factor when it comes to getting value from this data.
This is where HPC can make a huge difference, enabling both researchers and clinicians to ask bigger and more complex questions, and get the answers much faster. It leads directly to better research results and more precise treatment decisions.
Why bioinformatics matters
HPC is particularly useful in bioinformatics, which is a multidisciplinary field that integrates the principles of mathematics, statistics, computer science and biological science. The role of bioinformatics in medical research is to extract knowledge from biomedical data. HPC systems are now evolving to meet bioinformaticians' needs, with new hardware and software products allowing more sophisticated uses of data.
In life sciences, the time it takes to answer crucial questions is incredibly important. For instance, when it comes to seeing how a cancer patient will respond to a specific treatment, time matters a great deal. Speed is also imperative when spotting the signs of a new infection in society and stopping it before it spreads widely. The urgent need to create quick answers requires more powerful and sophisticated computing resources.
Understanding our genes
HPC enables us to not only be quick, but to also be accurate, allowing us to focus more on individual data. The life sciences industry is shifting from the development of blockbusters, which addresses the needs of the masses, to developing more niche, personalised solutions for patients. That's the promise of genomics, the study of the genome. Genomics is providing us with far more detailed understanding of what causes illness and infectious diseases and it's underpinning the development of innovations that would have been unthinkable even a decade ago.
Rapidly decreasing DNA sequencing costs, combined with increasing computing power, means that we are able to understand the human genetic code like never before. We are well placed to harness genomics to respond quickly to evolving threats, such as COVID-19,
as well as potential future pandemics. Genomics has the potential to revolutionise healthcare in many ways. It’s a game changer.
Through genomics, scientists can identify a drug target, often a protein that is either misbehaving or has behaviour that needs to be modulated. Once that protein is understood, it is important to think about how to make a small molecule that might actually interact with it. In order to do that, we need to understand and see the structure of the protein. For that, we need high-performance computing.
When we start to think about precision medicine, which takes into account genes and lifestyle for the individual patient, all of the data collected from edge devices - wearables, medical devices, IoT devices - need to show up and be computed at the same time. This must happen at a very high speed, which is where HPC comes in.
HPC solutions deliver scientific data at a significant speed, which allows integrators of HPC to break into the genomics space faster, without having to hire vertical technical expertise. By leveraging the technology, the time needed for scientific insights by turning genomics analytics can be reduced to minutes, which was a process that previously took days. Excessive time taken to analyse data impacts profits, growth, and delays time to scientific insights.
The role of AI
Artificial intelligence will also be important in everything from drug discovery to public health to the clinical setting.
Drug manufacturers frequently apply machine learning techniques to extract chemical information from large compound data sets and use this to design new drugs for clinical trials. AI models can be trained to better select the study participants with advanced statistical methods and to assess the results of the studies.
In the clinical setting, the potential of AI is enormous, ranging from the automation of diagnosis processes to therapeutic decision making and clinical research. Among the most promising applications of AI is for the automated processing of cardiac imaging data, which is necessary for the assessment of cardiac structure and function. Generation of more accurate and automated echocardiograms with the use of AI is expected to reveal unrecognised imaging features that will facilitate the diagnosis of cardiovascular disease. It will also minimise the limitations associated with human interpretation of these scans.
AI can assist in the public health domain as well, in preventing disease, prolonging life and promoting health. It can help identify specific demographics or locations where the prevalence of disease or high-risk behaviours exist, allowing doctors to intensify contact with patients as well as to target services to specific individuals.
The last application of AI is in the administration of healthcare. Healthcare systems are characterised by heavy administrative workflow. AI can perform different types of routines related to that administrative effort in a more efficient, accurate and unbiased fashion. A lack of bed availability in hospitals is an important cause of surgical cancellations and applying AI to optimise the availability of beds can help to decrease these.
HPC and AI in healthcare - the future
Looking ahead to the future, society must make it easier for healthcare organisations to use these important tools. Rather than research institutions taking the components and starting from scratch, we need to integrate these tools in a way that makes it easier for organisations in the healthcare space to make the most of them.
HPC can offer answers to many of the greatest problems we face, and combined with AI, can herald a new era of personalised medicine. It is therefore crucial that medical experts have the access they need to these game-changing technologies.