AI Healthcare

What is AI Healthcare?

Artificial intelligence (AI) healthcare is the use of machine learning (ML), natural language processing (NLP), deep learning (DL), and other AI-enabled tools to assist and, ideally, improve the patient experience, including diagnosis, treatment, and outcomes.

Why is AI important in healthcare?

AI in healthcare can be a critical tool for analyzing vast volumes of unique patient and raw medical information to create more accurate diagnoses and treatment plans. It can quickly analyze data from a variety of sources, identify potential problems, and recommend solutions across many contexts, including clinical and administrative environments.

How does AI impact the healthcare industry?

AI increases the velocity at which the healthcare industry can move by analyzing medical information via high performance computing (HPC). This data can include everything from medical imaging and diagnostics to surgical workflows. And this capability isn’t limited to a single location; cloud-based solutions can aggregate information from many networks and locations.

What are the benefits of AI in healthcare?

For clinical outcomes, AI analytics provides a faster, more in-depth examination of information without the risk of human error (for example, detecting tumors or precursors for disease). In turn, physicians and surgeons can leverage these results into better treatment options that can translate to improved outcomes. The processing power of AI isn’t limited to a case-by-case basis; it can take information from across the globe and uncover actionable insights that can lead to life-saving care and medical innovation. During the COVID-19 pandemic, for example, AI could be used to analyze new variants and create new, effective treatments faster than human-based research and assessment. In the past, AI has been essential for gene mapping and other pioneering genetics work.

For operational efficiencies, AI can identify opportunities to streamline processes, including surgery, and make them more productive. In turn, AI informs better decision-making by giving IT and medical administrators better visibility that can proactively avoid mistakes, resolve issues, and lower operating costs. Similar to improving patient medical outcomes, AI can improve how medical professionals and caregivers deliver care, either by having faster access to more patient records or by finding more efficient ways to manage patient care. With help from NPL, AI can even comb clinical notes (i.e., unstructured data), classify them, and use them to optimize clinical processes.

AI also helps medical organizations stay in compliance with enhanced security and safety. In addition to mitigating fraudulent access to private medical information, AI enables intelligent video analytics (IVA), letting staff members monitor their facilities and their patients. Using IVA and smart sensors, smart hospitals can recognize objects such as medical equipment and face coverings, identify and match faces of doctors and patients, and even detect elevated body temperatures. These inputs are used to determine high-risk individuals and create actionable outcomes.

What are the challenges of AI in healthcare?

Patient privacy and reliance on data analysis top the challenges for AI implementation. As more data is generated and used, healthcare organizations need the proper infrastructure in place to store and organize that data. Likewise, any AI needs the proper algorithms to make meaningful sense of any data pool. Without an effective infrastructure in place, organizations could risk misusing patient medical information or making it vulnerable to cyberattacks and other threats. Bad algorithms can also lead to unintentional biased decision-making. In other words, AI can inherit human biases. In one instance, a healthcare AI inadvertently deprioritized certain ethnicities for types of personalized care, while another discriminated against black patients when it came to kidney transplants. Beyond having the proper infrastructure, technology stack, and IT expertise in place, organizations will require fine-tuned AI codes of ethics to guide how data and AI are managed—counterparts to human-based ethical codes.

Then there are standard and rigorous security protocols. Compliance standards like HIPAA determine how patient information is kept private and used, which includes how AI accesses, analyzes, and uses data. Without proper protections, patient data could be used without consent, misappropriated, or be obtained (intentionally or by accident) by malicious actors.

HPE and AI healthcare

HPE is helping transform healthcare with AI and cloud-based solutions that can accelerate innovation while improving patient care. Healthcare institutions face a growing list of challenges, some due to the COVID-19 pandemic, others years in the making. A rapidly aging population and a rise in chronic conditions are driving up demand, while a growing shortage of healthcare workers and unsustainable increases in healthcare costs are straining providers and economies alike. These global trends have created a critical need for transformative innovation in the sector, combining increased efficiency with improved experience and outcomes.

Research institutions and medical facilities are using the ability to analyze massive data sets to sequence the human genome, develop new forms of treatment, speed and improve patient care, and better manage electronic health records. For medical and genomic research, platforms like HPE GreenLake for Healthcare use sophisticated analytics to select the best drug candidates and weed out those that are likely to be unsuccessful before incurring significant costs. With HPE, organizations can leverage cost-effective, scalable infrastructure that delivers the flexibility and performance required to support disparate teams of scientists and researchers conducting resource-intensive projects. Traditional infrastructure purchases require balancing current need against future demand, but HPE GreenLake provides pay-per-use, scale-up-and-down freedom: the ability to provision and pay for the resources organizations and teams need. Consumption-based billing ties costs to business outcomes, and a managed service makes more efficient use of in-house IT resources, freeing them up for more important tasks.