It’s impossible to escape the debate around artificial intelligence, especially in the healthcare field. Almost every group and provider is asking or being asked: “How can we use AI in our daily workflows?”
However, this question misses a key aspect when it comes to implementation: what kind of AI are you using in the first place? Using AI effectively means understanding the difference between edge AI and cloud AI in healthcare.
Defining Edge AI and Cloud AI
First, we must establish the differences between edge AI and cloud-based AI, and how the two are used. Both edge and cloud-based systems are based on deploying artificial intelligence already trained on massive amounts of data; the difference lies in how they’re deployed.
Edge AI
Edge AI operates at the “edge” of a computing system, closest to where the data lives in the real world. This means that the medical edge AI computer that supports the software is often in the same room as the object or patient the AI is analyzing.
The easiest example for this sort of system in healthcare is diagnostic systems used to identify visual markers of disease. Cameras feed information directly to the AI, which analyzes the images for visual markers such as esophageal lesions or the rate of blood flowing through capillaries, and alerts providers to these findings.
Cloud AI
Cloud AI systems run on remote data centers, typically run by third-party providers. Healthcare groups lack the resources or expertise to operate data centers of their own, and so they partner with services such as Microsoft Azure, Google Cloud, or AWS.
In a cloud AI structure, data is sent off-site to the AI system, including text, images, and questions, where the AI analyzes it using the centralized computing power of the server itself. Once the AI comes to a conclusion, it then transmits this answer or analysis back to the user. Access to the full server’s worth of information allows AI to develop deeper insights and draw more useful conclusions.
Advantages and Disadvantages of Edge AI and Cloud AI
Both edge AI and cloud AI have upsides and downsides that lend them towards specific roles.
Edge AI in Healthcare’s Advantages and Disadvantages
|
Advantages |
Disadvantages |
|
Faster processing with no latency from data transmission |
Performance is limited by hardware capabilities |
|
Zero reliance on network connectivity |
Fleet management concerns: devices must be maintained and updated by IT staff |
|
Private health information is processed locally, no concerns over sharing with non-healthcare entities |
Higher initial investment costs compared to conventional computer solutions |
|
Long-term costs can be lower depending on scale of operations |
Cloud AI in Healthcare’s Advantages and Disadvantages
|
Advantages |
Disadvantages |
|
Access to greater computing power for demanding workloads |
Latency can affect user experience and make cloud AI impractical for roles that require rapid changes |
|
No requirement for specialized hardware means easier implementation and lower barrier to entry |
Completely dependent on network connectivity: any disruption can affect performance |
|
Useful for large-scale analytics and tasks that need centralized data |
Sharing private health information with cloud service providers brings security and HIPAA compliance concerns |
|
Offloads IT maintenance to business partners |
Cloud storage and inference costs can rapidly expand depending on the scale of operations |
Key Factors for Your Decision
If you’re still struggling between using edge AI or cloud AI in healthcare, try answering these questions to determine which is better-suited to your needs:
How Time-Sensitive is the Role?
Is your potential AI use-case reliant on swift decision-making, or is it being used in roles where circumstances can change from moment to moment? If the answer to either of these questions is “yes,” the role is time-sensitive and cannot afford delays caused by data transmission.
In these situations, edge AI may be a better solution. Placing computing power as close as possible to the data source reduces transmission time, which can be considerable given the amount of information AI needs to process.
How Reliable is Your Network Connection?
Wireless network connections and periods of high network usage can lead to slow, unreliable communication between healthcare providers and any cloud service they’re using. This is less of an issue for providers at a permanent workstation, but for any mobile role, such as a bedside device, they will likely want to have their own AI computing hardware built into it.
How Compute-Intensive is the Workload?
Any AI task involves heavy amounts of parallel processing and often requires dedicated computer hardware, such as built-in GPUs. As the computational workload grows greater and greater, it becomes increasingly impractical for a healthcare group to host it on their own devices or servers.
Past a certain point, a healthcare group is simply better off by partnering with a cloud service provider, whose dedicated data centers and AI-focused architecture can process high-demand tasks more effectively.
How Sensitive is the Information?
Most information associated with patients and the conditions they’re experiencing is considered private and heavily regulated by laws such as HIPAA. This comes with serious requirements for data encryption and protection, and equally serious punishments for failing to meet these requirements.
Thus, sharing patient information with a cloud AI service provider carries a degree of risk. If your cloud service provider suffers a data breach, it will compromise you and your patients as well. For this reason, using edge AI computing in healthcare is often more secure, as the information stays within the healthcare group’s systems.
What Scale Are You Operating At?
How many providers and how many facilities do you plan to have using AI? If it’s a single team or a single facility, edge AI computers should be sufficient for their needs. However, if you plan to roll out AI-based workflows across your entire organization, it would require purchasing and deploying an exponentially greater number of devices.
Because cloud-based AI offloads hardware requirements to another company, it is far, far easier to scale to meet the wishes and needs of your healthcare group. This means you can expand your use of AI simply by purchasing a larger plan, rather than implementing new computers.
Edge AI Solutions from Cybernet Manufacturing
Ultimately, both edge AI and cloud-based AI have roles in healthcare. Determining which of these two technologies is better-suited for you will come down to your unique circumstances and how you plan to use it.
Whether you decide to embrace cloud AI or edge AI in healthcare, you’ll need reliable partners with the knowledge and experience to execute on your vision. If you need a source for medical edge AI computers, contact the experts at Cybernet Manufacturing. With decades of experience as an original equipment manufacturer, we can design, customize, and produce AI-ready computers for a wide range of healthcare tasks.