Artificial intelligence is already disrupting the healthcare sector, unlocking value and efficiencies as the sector tackles a global funding and skills gap.
Here are just four ways this transformative technology is being used in conjunction with secure cloud solutions to support medical staff, automate critical tasks, and boost patient outcomes.
1. Predicting patient flows and reducing pressure on healthcare facilities
The proportion of elderly people in developed economies is increasing dramatically, leading to an uptick in age-related illness and increased demand across healthcare.
In the absence of additional resources, artificial intelligence is unlocking new efficiencies by predicting patient flow.
“For example, Generative AI can analyze an individual patient’s medical history and symptoms, and then predict the length of their hospital stay,” explains Tim Hughes, Vice President, Client Partner, Healthcare and Life Sciences at EPAM Systems.
Scaling this approach to thousands of hospital admissions, using a secure cloud solution such as AWS, and medical facilities can model flows at scale and manage resources more effectively.
2. Monitoring patients from home for improved outcomes
Telemedicine has grown significantly in importance since the pandemic. Embedding AI is set to unleash even greater value.
Thanks to remote patient-monitoring devices and digitized health records held in the cloud, observations can now be made from home more holistically.
AI hosted in a secure cloud environment such as AWS can then collect and analyze this data in real-time, looking for anomalies and automatically escalating this information.
The global telemedicine market is predicted to more than triple in size during the next six years, reaching a value of $286.22 billion by 2030 with AI a driving force in this growth.1
3. Empowering clinicians to improve and accelerate scan diagnosis
“Medical scan analysis can be a hugely repetitive and labor-intensive exercise,” says Hughes.
“But cloud-based AI can automate this process, analyzing thousands of MRI, CT Scan and X-ray images and diagnosing medical issues at speed, often as well as or even better than a medic.”
For example:
- AI can be used to detect skin cancer.2
- To address “immense screening burden”, AI algorithms are being used to autonomously screen for diabetic retinopathy. The technology has returned strong performance, according to researchers.3
- AI-powered chest X-ray analysis can detect abnormalities just as, or more accurately than doctors.4 During one UK study AI software trained on 2.8 million chest X-rays analyzed patient imagery and generated a percentage possibility of abnormalities being present.5
4. Expediting critical equipment maintenance and maximizing uptime
From ventilators to linear accelerators and MRI scanners, modern medical facilities rely on complex technology. Maintaining this equipment can be complex however, and downtime impacts service levels.
AI-powered predictive maintenance, however, can pre-empt equipment failure enabling engineers to fix issues before they occur.
Machine learning can also calculate the average lifespan of a component based on historical data so that it can be replaced before a fault develops.
For example, Israel’s Beth Israel Deaconess Medical Center uses IoT sensors to monitor the performance of its anesthesia machines, enabling early fault detection, protecting patient safety and ensuring uptime.
The healthcare industry is being transformed by AI and big data. Secure, flexible and cost-effective cloud platforms, such as AWS, are acting as the bedrock of this change, however protecting sensitive patient data and ensuring it is accessible by the right healthcare professionals, for the right uses at the right time.
Discover how EPAM and its cloud partner AWS can help you boost productivity and patient outcomes in the healthcare sector.
1Fortune Business Insights: Telemedicine Market Size, Share, Growth | Global Report [2030] (fortunebusinessinsights.com)
2Oncology Nurse Advisor: Can Existing AI Models Accurately Detect Skin Cancer? – Oncology Nurse Advisor
3American Diabetes Association: Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness | Diabetes Care | American Diabetes Association (diabetesjournals.org)
4King’s College London, AI trained on X-rays can diagnose medical issues as accurately as doctors, https://www.kcl.ac.uk/news/ai-trained-on-x-rays-can-diagnose-medical-issues-as-accurately-as-doctors
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