Can AI help to save medicine?

Can AI help to save medicine?

Jack Watts, AI specialist, NetApp UK&I

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Health services have been stretched to their limits for years; however, thanks to COVID, the last 2 years have really compounded the issue. Physicians, nurses, and administrators are burning out. Data privacy and security are more important than ever. But at the same time, wasted expenditures amount to as much as 30% globally.??

Although there’s no way to completely alleviate the stress that healthcare professionals face, artificial intelligence (AI) is a strategic game changer. AI dramatically ?assists clinicians’ jobs and improves the patient experience, and it is also being used in the war against future COVID mutations[JW1]?.

Researchers at the University of Southern California have recently developed a machine learning (ML) model that creates vaccine design cycles in just seconds, rather than months or years.

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Streamlining administration

As medicine continues to evolve, patients, clinicians, and researchers are increasingly relying on AI to automate administrative tasks, streamline diagnosis, fast-track treatment research, predict risks, and manage public health.?

In the United States, one dollar in every three that is spent in the medical industry is for administrative costs alone. According to a report from the American Hospital Association, 40% of tasks performed by healthcare support staff and 33% of tasks performed by healthcare practitioners have the potential to be automated by AI. Automation can include scheduling appointments, inputting data into the electronic health record (EHR), and managing paperwork.

Scheduling appointments is time consuming, taking an average of 8 minutes per call. Self-scheduling technology based on AI offers 24/7 access and automatic reminders. And if a patient cancels their appointment, people on a waitlist are automatically notified of the opening. This automatic notification frees up front desk staff while also avoiding no-show appointments.

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Increased accuracy of diagnosis

When it comes to diagnostics, AI can help to increase accuracy based on deep learning (DL) and other methods. It also helps providers to proactively and efficiently manage care through risk score predictions and tailored treatment protocol recommendations.

Many cancers start with changes so small that no human could detect them, even with current medical imaging technology. AI programs armed with DL, however, can be “trained” to see the earliest changes in cell structure that typically lead to the development of cancerous cells. These programs alert oncologists to help them guide patient care protocols with greater accuracy and effectiveness. With all that’s expected of doctors, any level of efficiency gain or offloaded task can lighten the mental load.

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Fast-tracking research

With the help of AI, researchers can work rapidly to develop drugs and treatments that more effectively target specific diseases. From transforming disease identification to discovering how to prevent and treat diseases, AI plays a key role in improving the lives of billions of people.

For patients, AI means better experiences and more personalized support. From streamlining appointment scheduling and rescheduling to improving care outcomes while automating personalized support for many needs, AI can make life easier and minimize time spent on the phone or searching for answers.

But AI is not just for patients and clinicians. It has the potential to offer $70 billion in savings for the drug discovery process by 2028. With AI, huge datasets can be analyzed quickly and hidden insights easily extracted—both tasks that would be nearly impossible for individual researchers to accomplish. AI helps predict the chemical compound properties of a drug, saving researchers trial and error time and enabling the discovery of more effective drugs. Overall, AI can save hundreds of person hours in a laboratory, resulting in an accelerated drug discovery pipeline.

The three keys to AI success

The promise of AI to advance healthcare is exciting, but achieving maximum gains from it isn’t easy. The data must be available in the right place at the right time, while complying with strict regulations.

So data infrastructure really matters. Many AI architectures are unnecessarily complex, making them inefficient and difficult to scale. That’s why, when considering an AI strategy, it’s crucial to factor three things into planning:

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·??????Data volume.?Accuracy and data volume go hand in hand. And data volume compounds rapidly. The best-of-the-best ML and DL ?frameworks are key to efficiently training large AI models.

·??????Data movement.?Different AI applications need access to different types of structured and unstructured data across the edge and throughout clinical applications and cloud environments. An ecosystem-spanning data pipeline with built-in data protection capabilities is key.

·??????The need for speed.?When lives are on the line and clinicians and nurses are scrambling to keep up, instant response times are essential for all kinds of applications, from remote monitoring devices to imaging to managing nursing assistants.

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In the future, AI is likely to play an even greater role in medicine than it already is. However, in the highly regulated healthcare industry, striking the right balance between data movement needs and compliance requirements is especially challenging.

With the industry continuing to be under so much pressure, there’s a potential to limit some of this challenge by working with specialists who are knowledgeable about AI. ?Only then will clinicians be able to streamline tasks, improve the accuracy of diagnostics, and develop drugs more rapidly. ?