How and why AI diagnostics are changing hospital treatment

As technology advances, the opportunities for AI diagnostics to affect and augment healthcare delivery continue to expand. There is debate over whether these tools will one day replace physicians in the diagnostic process. To date, however, there are already several applications for these healthcare AI tools in hospitals.

AI is having a profound impact across multiple industries.  With its ability to learn from and perform tasks that typically require human intelligence, artificial intelligence (AI), and specifically machine learning, can help minimize errors, streamline processes, analyze large sets of data, and boost productivity and efficiency.  In healthcare, AI is being used in many ways. For instance, AI diagnostics can help physicians, clinicians and specialists diagnose diseases and conditions earlier which leads to more informed decision-making and a more efficient level of care.  

From disease detection to surgical efficiency to modernizing workflow management, these healthcare AI tools are revolutionizing the way hospitals function and deliver care.  According to the Government Accountability Office (GAO), “Machine learning technologies can help identify hidden or complex patterns in diagnostic data to detect diseases earlier and improvetreatments.”  The global market for AI diagnostics specific to cancer is projected to expand at a CAGR of 28.0% by 2023; these technologies could help detect hidden or complex patterns in diagnostic data that can help detect and treat the cancer earlier. 

With the power behind private 5G networks, the healthcare industry stands to gain enormously.  With 5G enhancing the power of AI diagnostics, hospitals could experience greater institutional efficacy, heightened productivity and provide more effective care.

AI diagnostics are machine learning tools designed to improve the overall delivery of healthcare services. While many AI algorithms are designed to assist with diagnosis, others are intended for use with patient risk identification or drug discovery. Additional AI are devoted to workflow management.

Many of these tools are equipped to evaluate large data sets. Based on these analyses, they can quickly offer actionable data and analysis that can affect clinical decision-making and patient outcomes. 

Within the healthcare industry, accurate diagnoses are the bedrock of successful patient care. However, every year, approximately 12 million Americans—roughly 5% of patients seeking outpatient care¹ —will receive an incorrect assessment of their condition. These mistakes can lead to delayed treatment, more severe disease, and even death.

In recent years, the use of AI diagnostics has expanded to address this problem. These healthcare AI tools are capable of recognizing disease in the earliest stages, frequently long before detection is possible with the human eye. As a result, patients can experience fewer missed or wrong diagnoses.

Earlier detection with medical AI may mean that patients experience fewer complications, and typically they have better outcomes. Diagnostic images—including X-rays, CT scans, MRIs and perfusion studies—that have traditionally been “read” by the human eye can now be augmented by real-time AI engines that can detect anomalies in seconds, and in many instances, detect them earlier and more accurately than human analysis, which could facilitate earlier intervention.  Plus, treatments may end up being less expensive for the individual and also less resource-heavy for the hospital.

As technology advances, the opportunities for AI diagnostics to affect and augment healthcare delivery continue to expand. There is debate over whether these tools will one day replace physicians in the diagnostic process. To date, however, there are already several applications for these healthcare AI tools in hospitals.

Hospitals rely on accurate MRI, CT, and X-ray interpretation to provide the highest level of care. Using quick, efficient data transmission, AI algorithms pull as much information together as possible to reconstruct the most detailed diagnostic images. Based on these recreations, these tools can detect abnormalities that may otherwise go unnoticed.

Procedures that rely on visual cues rather than lab tests, such as colonoscopies, can benefit from AI-enabled video diagnostic analysis. Using an AI inferencing system, hospitals can compare video captured through the endoscope to existing data on difficult-to-diagnose polyps. With a high-speed network, doctors can receive a near real-time alert to trigger a biopsy. These early warnings can mean the difference between a pre-cancer diagnosis or later-stage treatment.

Healthcare AI tools can contribute to greater safety in the surgical suite. For example, an AI-enabled camera can be programmed to track instruments used during a procedure—a job traditionally assigned to a surgical assistant. Not only can the camera provide a real-time notification of any missing instruments, but it also frees up space in the operating room.

AI diagnostics do more than impact patient care. They can also alleviate many of the workflow struggles healthcare providers face daily.

For several years, burnout has been a buzzword in the industry, and the pandemic has only exacerbated the problem. Today, nearly 63% of physicians report experiencing at least one burnout symptom. Many of these problems are linked to administrative tasks and a reduced capacity to interact with patients.

Implementing AI diagnostics and algorithms can help fix this issue. Within the next five years, industry estimates predict healthcare AI tools, such as near real-time natural language processing, could potentially reduce time spent on administrative tasks by roughly 50%.

The intent of AI diagnostics is to enhance and accelerate the delivery of care. If these tools can't perform tasks faster than human healthcare providers, they can't make an impact on patient care and outcomes. Consequently, the underlying requirements of any network to support these algorithms are high speed and low latency.

5G and mobile-edge computing (MEC), such as Verizon 5G Edge, can fulfill this need. Compared to 4G technology, 5G speeds are up to 10 times faster. MEC also doubles down on the low latency available through 5G by providing short, high-volume connections between applications, devices, and end users. This rapid information flow helps providers with timely clinical decision-making, particularly in emergency scenarios.

A hospital infrastructure powered by enterprise intelligence creates the most effective environment for patients and providers alike. It provides the near real-time connectivity needed for AI diagnostics to position those insights at the point of care exactly when they’re needed for clinical decision-making.  In fact, according to a HIMSS Market Insights survey, 57% of medical IT practitioners believe 5G will have a significant or moderate positive impact on AI and its use in diagnostics².

5G offers the extreme low latency needed to power fast, precise detection. For example, the aforementioned high-fidelity imaging used for cancer screening, such as identifying polyps with colon cancer, demands a significant level of sensitivity. Catching the slightest abnormalities necessitates latency requirements that can fall as low as sub-20 milliseconds.