Who knew that GPUs, originally made to jazz up computer game graphics, would someday become healthcare heroes? The healthcare industry struggles to analyse millions of records. Privacy regulations and data localisation laws further complicate system access and processing records. Time-critical applications such as digital health, where query run times can influence life-threatening decisions, have also been practically impossible. But GPU’s parallel architecture allows them to crunch through large medical datasets and train complex neural networks much faster than regular CPUs. For example, enterprise GPUs like the NVIDIA Ampere A100 provide up to 70 times more cores at a fraction of the cost when compared to equivalent CPUs.
Such advancements are breaking the barriers for the healthcare industry to accelerate AI and ML tasks with the help of GPUs. In this article, we will be understanding the role of GPUs in healthcare applications.
The major advances in Healthcare came from the work of Esteva who used a deep-learning algorithm to diagnose melanoma. By leveraging a large dataset of over 100,000 images of malignant skin lesions, their Inceptionv4 model learned subtle visual patterns that can distinguish different forms of melanoma with high accuracy.
Specifically, when evaluated on 100 test cases containing 75 benign moles and 25 melanoma samples, the deep learning algorithm achieved an impressive 95% sensitivity and 64% specificity in detecting tumours. By comparison, an average dermatologist scored 86.6% and 71.3% on these metrics respectively. This indicates AI can diagnose melanoma at an expert human level while continuing to improve through continuous training.
The report further concludes that these AI systems require active collaboration between technology and healthcare organisations. The scalability of AI applications in healthcare relies heavily on continuous GPU advancements enabling ever-more complex neural networks to be trained on exponentially growing data. Cloud-based services with large-scale GPU clusters are already democratising this advantage - allowing hospitals to develop highly accurate AI tools for early disease detection without massive capital investments.
Medical images like X-rays, CT scans, MRIs, and pathology slides are rich in detail but require substantial computing power to process using AI. GPU in medical imaging can speed up the training of convolutional neural networks on such imaging data tremendously compared to using CPUs alone. For example, diagnoses based on medical images rely extensively on subtle visual features that neural networks can extract by learning from hundreds of thousands of expert-labelled examples. GPUs facilitate fast iteration so models can learn these complex patterns.
Genomic medicine is another domain where GPUs drive progress. Whole genome sequencing of patients generates terabytes of data requiring analysis. Deep learning on GPU clusters can spot important mutations, gene expressions, and biomarkers indicating disease risk or treatment efficacy. Pharma researchers also employ GPUs to discover new drug compounds and their interactions by screening molecular datasets and simulating biological processes.
Early and accurate diagnosis of diseases like cancer and neurological disorders can improve patient outcomes significantly. Here too GPUs help by enabling real-time inference of symptoms from sensor data. Wearable medical devices can continuously monitor patients and relying on GPU-powered AI run locally or in the cloud, trigger clinicians' alerts about abnormal health events.
With NVIDIA Healthcare GPUs for Machine Learning, healthcare organisations can significantly boost their medical image analysis (e.g., MRI, CT scans), disease detection and diagnosis and drug discovery and genomics. We recommend using the NVIDIA H100 SXM, NVIDIA H100 PCIe and NVIDIA A100 to achieve faster training, advanced model analysis, and accurate predictions.
Let’s understand how GPU-accelerated AI is used in Healthcare:
AI-powered scanning technology can automatically classify documents by examining metadata. This facilitates precise information extraction from scans. AI also shows promise in processing ultrasound images to identify clinical conditions quickly. However, a limitation currently is that AI models only utilise visual imaging data.
An emerging focus in healthcare AI is therefore building diagnostic systems that assimilate both visual inputs like medical scans as well as patient electronic health records. This allows for more holistic and personalised assessments. Integrating both imaging and text data on symptoms, history, and demographics can improve the accuracy and reliability of AI prediction.
Ultimately, applying AI to enhance existing solutions like scanning and ultrasound offers faster and more accurate diagnostics. As research advances, AI has the potential to enable more preventative and patient-centric care by extracting insights from multidimensional patient data.
Healthcare organisations are adopting AI-based tools to efficiently gather, format, store, retrieve and track customer information. Robotic process automation enables seamlessly collating patient data from disparate systems, overcoming previous manual efforts. Integrating powerful AI algorithms allows making this patient data readily available to healthcare personnel when needed.
Effectively utilising patient data has huge implications in healthcare. Valuable insights about diseases, medications and outcomes lie hidden in siloed patient records. Without aggregating and connecting these data puzzles, crucial health and treatment insights get overlooked. This ends up costing billions in missed opportunities.
Using AI-powered data management and analytics, healthcare providers can tap into their wealth of patient data. Identifying correlations and patterns can inform improved diagnostics and personalising treatment. Automating redundant data tasks also lets healthcare refocus resources on value-added analysis. In summary, AI-enabled data administration not only optimises operations but also enhances life-saving medical research.
Traditional drug discovery is a time-consuming and resource-intensive process, often spanning over a decade for a drug to reach the market, and a 10% success rate for drug candidates.
Artificial intelligence-enabled drug development systems are assisting businesses in utilising massive amounts of data to swiftly identify patient response markers and create more effective and affordable appropriate treatment options.
Generative AI, leveraging large language models, presents an opportunity to enhance success rates within a shorter timeframe and at a reduced cost. Much like the text generation capabilities of services such as ChatGPT, generative AI models trained on biomolecular data can play a crucial role in producing blueprints for novel molecules and proteins, a key phase in drug discovery.
NVIDIA Healthcare solution called BioNeMo is a cloud-based solution for applying generative AI to biology, providing a diverse range of AI models dedicated to small molecules and proteins. By using BioNeMo, professionals in pharmaceutical research and industry can leverage generative AI to expedite the identification and optimisation of potentially new drug candidates.
Healthcare assistants, often in the form of virtual health assistants, play a multifaceted role encompassing tasks such as handling routine patient calls and emails, safeguarding medical information, scheduling doctor appointments, sending reminders for follow-up visits and clinical meetings, and more.
These systems integrate cognitive computing, augmented reality, and the analysis of body and voice movements. The synergy of cognitive computing and augmented reality facilitates the simulation and resolution of intricate human thoughts. This form of AI in healthcare proves highly beneficial by offering patients a personalised experience in managing their health and addressing their queries. The reduction in hospital visits is advantageous for both patients and medical professionals.
Some challenges come along with the adoption of GPUs to accelerate A/ML workloads in the healthcare sector:
Cost and Infrastructure: Implementing GPU technology in healthcare AI comes with high upfront costs including the acquisition of high-performance GPUs and the necessary infrastructure to support them. The expense associated with upgrading existing systems and ensuring compatibility with GPU-based solutions can pose financial challenges for healthcare organisations, especially smaller ones with limited budgets. With Hyperstack’s affordable cloud GPU pricing, you can access powerful NVIDIA GPUs like the NVIDIA H100 specialised for AI.
Data Security and Privacy Concerns: Healthcare organisations have sensitive patient data, and the adoption of GPU-driven AI raises concerns about data security and privacy. Ensuring compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) and other global health data security laws becomes paramount. The integration of GPUs must align with robust security measures to safeguard patient information from potential breaches or unauthorised access.
Integration with Existing Systems: Many healthcare institutions have established legacy systems that may not be seamlessly compatible with GPU-accelerated AI solutions. Integrating GPUs into existing infrastructure can be a complex process, requiring careful planning and execution to prevent disruptions in healthcare workflows. Compatibility issues and the need for system upgrades can create challenges in the smooth integration of GPU technology.
Healthcare is undergoing a major technological transformation. Many companies are using artificial intelligence and machine learning to analyse medical data in new ways. The goal is to detect diseases sooner, personalise treatment plans, and improve patient outcomes.
Image Source: Global Generative AI in Healthcare Market By Application Report (Market.us)
For example, a German startup MedaPlus developed an AI-assisted software that listens to heart and breathing sounds. The AI algorithms can detect abnormalities and warn doctors early on about potential issues. Many companies are also using AI to analyse CT scans, genetic tests, ECG readings - you name it. The technology helps radiologists and doctors spot problems quicker and with less room for human error.
AI-powered chatbots and virtual assistants are also on the rise. They can collect health data from wearables, track symptoms, provide personalised recommendations, and even diagnose basic conditions. This allows people access to healthcare advice conveniently from their smartphones. Pretty amazing, if you ask me!
The applications go on and on. AI programmes can monitor recovering surgery patients, assist in complex procedures, and deliver medical supplies by drones. A US-based startup RIF Robotics offers an autonomous surgical tray assembler robot. It utilises image segmentation and object classification ML models to identify, grab, and arrange all tools surgeons need to operate. The sky's the limit. Or I guess not the sky, since drones have limits on how high they can fly - but you get the point!
And of course, anytime you're dealing with sensitive medical records and devices, there are also cybersecurity concerns. Companies are working on AI-based solutions to detect threats, prevent fraud, control access, and keep these healthcare networks secure. There’s another German startup aiming to reinforce privacy defences for electronic health records. XetaX developed an encrypted database called XHR that allows medical data analysis while locking down personal details. They apply some advanced cryptographic techniques called "homomorphic encryption" to enable computations on sensitive data while keeping it all safe. Securing medical data as we transition towards more connected healthcare is so crucial. XetaX's focus on encryption ensures life-saving health tech doesn't hijack personal privacy. Patients stay empowered and in control of their records.
In conclusion, GPUs play an important role in improving AI capabilities across the healthcare ecosystem. From speeding up the analysis of medical images and genomic datasets to enabling personalised predictive diagnosis - GPUs are truly the game changers. Their ability to massively parallel process visual, genetic, and sensor data facilitates tackling previously invincible healthcare challenges. As patient datasets grow and AI models become more complex, GPU acceleration is fundamental to tackling life-saving research and bringing smarter treatments into clinical workflows.
Don't let infrastructure hold back your healthcare innovation. Our NVIDIA cloud GPUs offer on-demand scalability and flexibility, so you can focus on what matters most - advanced healthcare. Sign up now to get started!
GPU can be used for medical imaging processing to speed up the training of convolutional neural networks on such imaging data tremendously compared to using CPUs alone. For example, diagnoses based on medical images rely extensively on subtle visual features that neural networks can extract by learning from hundreds of thousands of expert-labelled examples. GPUs facilitate fast iteration so models can learn these complex patterns.
The NVIDIA A100 is one of the best GPUs for medical image analysis. Our NVIDIA A100 80GB PCIe starts at $ 2.75 per hour. Check our cloud gpu pricing.
Some key challenges with integrating GPUs into healthcare systems:
High upfront costs of GPU hardware and infrastructure upgrades are especially challenging for smaller organisations with limited budgets. Cloud solutions can help mitigate these costs.
Ensuring robust data security and privacy measures are in place to comply with regulations like HIPAA and safeguard sensitive patient data from potential breaches with the new GPU systems.
Complex integration with legacy healthcare IT systems may require upgrades and careful planning to avoid disrupting critical workflows and operations during the transition to GPU-powered systems.
GPUs can accelerate tasks such as medical image processing, genomic sequencing, and accurate prediction, leading to faster disease diagnosis and treatment.