Three Ways AI Will Supercharge Lab Diagnostics

From aggregating massive patient datasets to unlocking new insights, artificial intelligence is set to transform clinical laboratories for the better.

AsianScientist (Mar. 10, 2021) – In a world where personalized song recommendations and smart voice assistants are the norm, artificial intelligence (AI) has quietly become an integral part of various industries including finance, marketing and logistics.

The healthcare industry is no exception, as AI is increasingly applied in clinical practice. Indeed, the pandemic has given an opportunity for AI to shine—with powerful algorithms now used to find the SARS-CoV-2 virus’ weak spots and identify the optimal combination of drugs to treat COVID-19.

In clinical laboratories, AI is starting to gain traction. A recent survey of 128 US-based laboratory employees found that AI is only used in 15.6 percent of laboratories. However, 66.4 percent of the survey respondents felt that they might use AI in the future.

With further research and innovation, AI could make clinical laboratories more efficient, allow them to provide more insights and assist in synthesizing data from many different sources. Here are three ways AI could change clinical laboratories for the better.

1. Making conventional methods more powerful

According to Professor Hao Xiaoke, Chief Scientist at the Greater Xi’an Area Medical Diagnostic Lab Center, clinical laboratories must adopt intelligent technologies like AI to address the growing burden of cancer. Watch his full interview with Roche Diagnostics here.

While biopsies remain a routine—if invasive—technique for cancer diagnosis, serum tumor markers (STMs) have also become an indispensable part of the cancer detection toolkit. Not only are they non-invasive, but even small volumes of STMs can be used in diverse applications—from evaluating response to different therapies to monitoring potential cancer recurrence.

For all the benefits of STMs, innovations in data science, AI and machine learning are set to expand their clinical utility. Through AI, data from various sources—including imaging results, molecular tests and medical histories—can be easily incorporated with STM results, enabling clinicians to select more targeted treatment recommendations.

With applications like these, AI is set to play an important role in helping laboratories interpret multivariate datasets in new ways. Though more research is needed to seamlessly bridge algorithms to patient care, AI can help STMs fulfill their potential in a wider variety of cancers and beyond.

2. Taking remote service to the next level

Integrating AI into laboratory equipment can improve predictive maintenance, reducing downtime and ensuring greater operational efficiency in the process. Photo credit: Freepik.

Across the world, laboratories are turning to remote service systems to keep their instruments in check. With these systems, instrument usage data is sent to remote service providers in real-time, allowing them to easily alert laboratory staff when instruments are likely to need repairs. In doing so, clinical laboratories can reduce downtime by preventing instrument failures in the first place!

As convenient as this may already be, AI could further refine remote service. Armed with AI, these systems could proactively analyze instrument data and automatically send alerts to service technicians—saving laboratory staff the extra step of contacting the technicians themselves.

Though AI-powered remote service may make the job of laboratory managers easier, it’s important to note that it is patients who will ultimately benefit. After all, smoother laboratory operations will lead to fewer instances of test failures and overall, a better patient experience.

3. Powering precision medicine

Group Chief Technology Officer Professor Ngiam Kee Yuan of the National University Health System is pioneering the use of AI in healthcare innovation. Watch his recent talk at RED2020 here.

The era of one-size-fits-all medicine is long over. Today, precision medicine is the name of the game—taking into account each patient’s unique genetic, lifestyle and environmental data to better predict disease risk, diagnose disease and provide tailored therapies.

Given the deluge of data required by precision medicine, Singapore’s National University Health System (NUHS) has developed an all-encompassing platform called Discovery AI that ties in different AI tools to enhance healthcare delivery. Launched in 2018, Discovery AI synthesizes massive amounts of real-time patient data like medical and treatment histories, with efforts to include genetic information and other forms of data already underway

The platform can then be used to testbed new laboratory applications of AI and improve existing ones within an integrated setting. With Discovery AI, NUHS laboratories can someday better analyze tests in the context of each patient’s comprehensive data—generating far more exact clinical insights and unlocking the potential of precision medicine.

For a deeper dive into how AI is shaping the future of clinical laboratories, visit Lab Insights, an educational content platform hosted by Roche Diagnostics Asia Pacific.

Asian Scientist Magazine is a media partner of Roche Diagnostics Asia Pacific.


Copyright: Asian Scientist Magazine; Photo: Freepik.
Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.

Asian Scientist Magazine is an award-winning science and technology magazine that highlights R&D news stories from Asia to a global audience. The magazine is published by Singapore-headquartered Wildtype Media Group.

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