How Technology Can Speed Up A Lupus Clinical Trial

February 14, 2022

Lupus is a chronic, multi-systemic autoimmune illness that affects millions of people worldwide; more than 90% of these are women.

It usually occurs between 15 and 45, when they are most likely to have children. In addition, African Americans, Latinos, Asians, and Native Americans are two to three times more likely than Caucasians to develop it. The immune system produces antibodies that can attack any body area when you have lupus, including the kidneys, brain, heart, lungs, blood, skin, and joints. So, how can technology help us fight this? Well, companies like Clinical Ink are using technology to aid their research, and here’s how.

Most clinical operations professionals understand and agree that enhancing the speed and efficiency of clinical trials is a top objective. What is less clear is which approaches in this area have worked well and which have failed in recent years, as biopharmaceutical sponsors deal with a changing drug development environment influenced by regulatory pressures, rising R&D costs, rising healthcare spending, and the challenge of rushing trials of experimental drugs and vaccines in response to outbreaks.

Through the use of novel outcomes, better patient engagement, reduced patient burden, and improved trial management, technology advances can improve efficiency and productivity. However, to use new technology in clinical trial design, the drug development community will need to incorporate lessons acquired from other industries, such as a greater focus on the consumer. Today, most clinical trial volunteers have a high level of experience and comfort with technology in their daily lives.

Biopharmaceutical companies could use technology to extract valuable data from clinical trials and reduce product development cycle times. Patients may also get benefits, such as improved satisfaction ratings and better overall experiences throughout the experiment.

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One of the critical advancements within this sector is Artificial Intelligence. In a world where time is of the essence, AI can help considerably speed up processes; this not only saves time but can save thousands, if not millions of dollars. Artificial intelligence is on the verge of revolutionizing the healthcare business. AI is already speeding up clinical studies in labs and hospitals. The average clinical trial takes years to complete, costs hundreds of millions of dollars — if not billions of dollars — and only 14 percent of medications that enter clinical trials receive FDA approval.

AI assists businesses and organizations in using software that allows for faster and more accurate data collection. For example, for vaccines and medicinal pharmaceuticals, machine-learning technology can reduce time-to-market by up to 300 percent, or the time between the invention of a product idea and its sale.

Typical medication studies necessitate microscope images of every reaction that occurs when a cell representing a sickness or illness is exposed to various substances; these trials can produce hundreds of millions of snapshots. Rather than taking new photos for each different cell-compound reaction, scientists may use AI to forecast how cells will behave based on recycled snapshots.

When problems in clinical trials occurred in the past, teams had to check throughout their infrastructure and applications to figure out what was causing the issues, which may take teams offline for an extended period. The clinical trial process benefits from AI technology in three ways: it becomes faster, more reliable, and secure, allowing researchers to focus on the most crucial trial areas that require human attention. In addition, when a problem develops in a clinical study, an AI system can immediately identify which line of code or gadget is to blame, allowing researchers to focus their time and effort on other tasks.

While all AI is not the same, the ultimate goal of any machine-learning software is to speed up laborious operations that took humans a long time to do in the past, even if they didn’t involve much brainpower. Humans can use AI to allocate that time to jobs that require human judgment.

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